Real-time adaptive control of additive manufacturing processes using machine learning

ABSTRACT

Disclosed herein are machine learning-based methods and systems for automated object defect classification and adaptive, real-time control of additive manufacturing and/or welding processes.

CROSS-REFERENCE

This application is a continuation of U.S. patent application Ser. No.15/604,473 filed May 24, 2017, the contents of which are herebyincorporated by reference in their entirety.

BACKGROUND

Additive manufacturing processes are fabrication techniques that allowone to produce functional complex parts layer by layer, without the useof molds or dies. Despite recent advances in the methods and apparatusused for various types of additive manufacturing, a need exists formethods that allow rapid optimization and adjustment of the processcontrol parameters used in response to changes in process orenvironmental parameters, as well as for improving the quality of theparts that are produced. Methods and systems are disclosed forperforming automated classification of object defects using machinelearning algorithms. Also disclosed are methods and systems forperforming real-time adaptive control of free form deposition or joiningprocesses, including additive manufacturing or welding processes, toimprove process yield, throughput, and quality.

SUMMARY

Disclosed herein are methods for real-time adaptive control of a freeform deposition process or a joining process, the methods comprising: a)providing an input design geometry for an object; b) providing atraining data set, wherein the training data set comprises processsimulation data, process characterization data, in-process inspectiondata, post-build inspection data, or any combination thereof, for aplurality of design geometries or portions thereof that are the same asor different from the input design geometry of step (a); c) providing apredicted optimal set or sequence of one or more process controlparameters for fabricating the object, wherein the predicted optimal setof one or more process control parameters are derived using a machinelearning algorithm that has been trained using the training data set ofstep (b); and d) performing the free form deposition process or thejoining process to fabricate the object, wherein real-time processcharacterization data is provided as input to the machine learningalgorithm to adjust one or more process control parameters in real-time.

In some embodiments, steps (b)-(d) are performed iteratively and processcharacterization data, in-process inspection data, post-build inspectiondata, or any combination thereof for each iteration is incorporated intothe training data set. In some embodiments, the free form depositionprocess or joining process is a stereolithography (SLA), digital lightprocessing (DLP), fused deposition modeling (FDM), selective lasersintering (SLS), selective laser melting (SLM), or electronic beammelting (EBM), or welding process. In some embodiments, the free formdeposition process is a liquid-to-solid free form deposition process. Insome embodiments, the liquid-to-solid free form deposition process is alaser metal-wire deposition process. In some embodiments, the processsimulation data is provided by performing finite element analysis (FEA),finite volume analysis (FVA), finite difference analysis (FDA),computational fluid dynamics (CFD) calculations, or any combinationthereof. In some embodiments, the one or more process control parametersto be predicted or controlled comprise a rate of material deposition, arate of displacement for a deposition apparatus, a rate of accelerationfor a deposition apparatus, a direction of displacement for a depositionapparatus, a location of a deposition apparatus as a function of time (atool path), an angle of a deposition apparatus with respect to adeposition direction, an angle of overhang in an intended geometry, anintensity of heat flux into a material during deposition, a size andshape of a heat flux surface, a flow rate and angle of shielding gasflow, a temperature of a baseplate, an ambient temperature controlduring a deposition process, a temperature of a deposition materialprior to deposition, a current or voltage setting in a resistive heatingapparatus, a voltage frequency or amplitude in an inductive heatingapparatus, a choice of deposition material, a ratio by volume or a ratioby weight of deposition materials if more than one deposition materialis used, or any combination thereof. In some embodiments, the processsimulation data comprises a prediction of a bulk or peak temperature ofa deposited material, a cooling rate of a deposited material, a chemicalcomposition of a deposited material, a segregation state of constituentsin a deposited material, a geometrical property of a deposited material,an intensity of heat flux out of a material during deposition, anelectromagnetic emission from a deposition material, an acousticemission from a deposition material, or any combination thereof, as afunction of a set of specified input process control parameters. In someembodiments, the process characterization data comprises a measurementof a bulk or peak temperature of a deposited material, a cooling rate ofa deposited material, a chemical composition of a deposited material, asegregation state of constituents in a deposited material, a geometricalproperty of a deposited material, a rate of material deposition, a rateof displacement for a deposition apparatus, a location (tool path) of adeposition apparatus, an angle of a deposition apparatus with respect toa deposition direction, a deposition apparatus status indicator, anangle of overhang in a deposited geometry, an angle of overhang in anintended geometry, an intensity of heat flux into a material duringdeposition, an intensity of heat flux out of a material duringdeposition, an electromagnetic emission from a deposition material, anacoustic emission from a deposition material, an electrical conductivityof a deposition material, a thermal conductivity of a depositionmaterial, a defect in the geometry of an object being fabricated, or anycombination thereof. In some embodiments, the in-process or post-buildinspection data comprises data from a visual or machine vision-basedinspection of surface finish, a visual or machine vision-basedinspection of surface cracks and pores, a test of a mechanical propertysuch as strength, hardness, ductility, fatigue, a test of a chemicalproperty such as composition, segregation of constituent materials, adefect characterization methodology such as X-ray diffraction orimaging, CT scanning, ultrasonic imaging, Eddy current sensor arraymeasurements, or thermography, or any combination thereof. In someembodiments, the machine learning algorithm comprises a supervisedlearning algorithm, an unsupervised learning algorithm, asemi-supervised learning algorithm, a reinforcement learning algorithm,a deep learning algorithm, or any combination thereof. In someembodiments, the machine learning algorithm comprises an artificialneural network algorithm, a Gaussian process regression algorithm, alogistical model tree algorithm, a random forest algorithm, a fuzzyclassifier algorithm, a decision tree algorithm, a hierarchicalclustering algorithm, a k-means algorithm, a fuzzy clustering algorithm,a deep Boltzmann machine learning algorithm, a deep convolutional neuralnetwork algorithm, a deep recurrent neural network, or any combinationthereof. In some embodiments, the machine learning algorithm comprisesan artificial neural network. In some embodiments, the artificial neuralnetwork comprises an input layer, an output layer, and at least 1 hiddenlayer. In some embodiments, the artificial neural network comprises aninput layer, an output layer, and at least 5 hidden layers. In someembodiments, the artificial neural network comprises an input layer, anoutput layer, and at least 10 hidden layers. In some embodiments, thenumber of nodes in the input layer is at least 10. In some embodiments,the number of nodes in the input layer is at least 100. In someembodiments, the number of nodes in the input layer is at least 1,000.In some embodiments, at least one stream of process characterizationdata is provided to the machine learning algorithm at a rate of at least10 Hz. In some embodiments, at least one stream of processcharacterization data is provided to the machine learning algorithm at arate of at least 100 Hz. In some embodiments, at least one stream ofprocess characterization data is provided to the machine learningalgorithm at a rate of at least 1,000 Hz. In some embodiments, the oneor more process control parameters are adjusted at a rate of at least 10Hz. In some embodiments, the one or more process control parameters areadjusted at a rate of at least 100 Hz. In some embodiments, the one ormore process control parameters are adjusted at a rate of at least 1,000Hz. In some embodiments, the method is implemented using a singleintegrated system comprising a deposition apparatus, a sensor, and aprocessor. In some embodiments, the method is implemented using adistributed, modular system comprising a first deposition apparatus, afirst sensor, and a first processor, wherein the first depositionapparatus, the first sensor, and the first processor are configured toshare training data and/or real-time process characterization data via alocal area network (LAN), an intranet, an extranet, or an internet. Insome embodiments, the training data set resides in the internet cloud.In some embodiments, the sharing of data between the first depositionapparatus, the first sensor, and the first processor is facilitated byuse of a data compression algorithm, a data feature extractionalgorithm, or a data dimensionality reduction algorithm. In someembodiments, the training data set is shared between and updated usingdata from a plurality of deposition apparatus and sensors that areconfigured to share data via a local area network (LAN), an intranet, anextranet, or an internet. In some embodiments, the training data setfurther comprises process characterization data, in-process inspectiondata, post-build inspection data, or any combination thereof, that isgenerated by a skilled operator while manually adjusting the inputprocess control parameters. In some embodiments, as part of the trainingof the machine learning algorithm, the machine learning algorithmrandomly chooses values within a specified range for each of a set ofone or more process control parameters, and incorporates the resultingprocess simulation data, process characterization data, in-processinspection data, post-build inspection data, or any combination thereof,into the training data set to improve a learned model that maps processcontrol parameter values to process outcomes.

Also disclosed herein are systems for controlling a free form depositionprocess or a joining process, the systems comprising: a) a firstdeposition apparatus, wherein the deposition apparatus is capable offabricating an object based on an input design geometry; b) one or moreprocess characterization sensors, wherein the one or more processcharacterization sensors provide real-time data for one or more processparameters or object properties; and c) a processor programmed to (i)provide a predicted optimal set of one or more input process controlparameters, and (ii) to adjust one or more process control parameters inreal-time based on a stream of real-time process characterization dataprovided by the one or more process characterization sensors, whereinthe predictions and adjustments are derived using a machine learningalgorithm that has been trained using a training data set.

In some embodiments, the system further comprises a computer memorydevice within which machine learning algorithm software, sensor datafrom the one or more process characterization sensors, predicted oradjusted values of one or more process control parameters, the trainingdata set, or any combination thereof, is stored. In some embodiments,the first deposition apparatus, the one or more process characterizationsensors, and the processor are incorporated into a single integratedsystem. In some embodiments, the first deposition apparatus, the one ormore process characterization sensors, and the processor are configuredas distributed system modules that share training data and/or real-timeprocess characterization data via a local area network (LAN), anintranet, an extranet, or an internet. In some embodiments, the trainingdata set resides in the internet cloud, and is shared between andupdated using data from a plurality of deposition apparatus and sensorsthat are configured to share data via a local area network (LAN), anintranet, an extranet, or an internet. In some embodiments, the trainingdata set comprises process simulation data, process characterizationdata, in-process inspection data, post-build inspection data, or anycombination thereof, for a plurality of objects that are the same as ordifferent from the object of step (a). In some embodiments, the one ormore process characterization sensors comprise temperature sensors,position sensors, motion sensors, touch/proximity sensors,accelerometers, profilometers, goniometers, image sensors and machinevision systems, electrical conductivity sensors, thermal conductivitysensors, strain gauges, durometers, X-ray diffraction or imagingdevices, CT scanning devices, ultrasonic imaging devices, Eddy currentsensor arrays, thermographs, deposition apparatus status indicators, orany combination thereof. In some embodiments, the one or more processcharacterization sensors comprise at least one laser interferometer,machine vision system, or sensor that detects electromagnetic radiationthat is reflected, scattered, absorbed, transmitted, or emitted by theobject. In some embodiments, the machine vision system is configured asa visible light-based system used for measurement of object dimensions.In some embodiments, the machine vision system is configured as avisible light-based system used for measurement of object surfacefinish. In some embodiments, the machine vision system is configured asan infrared-based system used for measurement of object temperature orheat flux within the object. In some embodiments, the machine visionsystem is configured as an X-ray diffraction-based system used formeasurement of object material properties. In some embodiments, the oneor more process control parameters to be predicted or adjusted comprisea rate of material deposition, a rate of displacement for a depositionapparatus, a rate of acceleration for a deposition apparatus, adirection of displacement for a deposition apparatus, an angle of adeposition apparatus with respect to a deposition direction, anintensity of heat flux into a material during deposition, a size andshape of a heat flux surface, a flow rate and angle of shielding gasflow, a temperature of a deposition apparatus, an ambient temperaturecontrol during a deposition process, a temperature of a depositionmaterial prior to deposition, a current or voltage setting in aresistive heating apparatus, a voltage frequency or amplitude in aninductive heating apparatus, a choice of deposition material, a ratio byvolume or a ratio by weight of deposition materials if more than onedeposition material is used, or any combination thereof. In someembodiments, the machine learning algorithm comprises a supervisedlearning algorithm, an unsupervised learning algorithm, asemi-supervised learning algorithm, a reinforcement learning algorithm,a deep learning algorithm, or any combination thereof. In someembodiments, the machine learning algorithm comprises an artificialneural network. In some embodiments, the artificial neural networkcomprises an input layer, an output layer, and at least 5 hidden layer.In some embodiments, the number of nodes in the input layer is at least100. In some embodiments, at least one stream of real-time processcharacterization data is provided to the machine learning algorithm at arate of at least 100 Hz. In some embodiments, the one or more processcontrol parameters are adjusted at a rate of at least 100 Hz.

Disclosed herein are methods for automated classification of objectdefects, the methods comprising: a) providing a training data set,wherein the training data set comprises fabrication process simulationdata, fabrication process characterization data, in-process inspectiondata, post-build inspection data, or any combination thereof, for aplurality of design geometries that are the same as or different fromthat of the object; b) providing one or more sensors, wherein the one ormore sensors provide real-time data for one or more object properties;c) providing a processor programmed to provide a classification ofdetected object defects using a machine learning algorithm that has beentrained using the training data set of step (a), wherein the real-timedata from the one or more sensors is provided as input to the machinelearning algorithm and allows the classification of detected objectdefects to be adjusted in real-time.

In some embodiments, the method further comprises removing noise fromthe object property data provided by the one or more sensors prior toproviding it to the machine learning algorithm. In some embodiments,noise is removed from the object property data using a signal averagingalgorithm, smoothing filter algorithm, Kalman filter algorithm,nonlinear filter algorithm, total variation minimization algorithm, orany combination thereof. In some embodiments, the one or more sensorsprovide data on electromagnetic radiation that is reflected, scattered,absorbed, transmitted, or emitted by the object. In some embodiments,the one or more sensors comprise image sensors or machine visionsystems. In some embodiments, the electromagnetic radiation isultraviolet, visible, or infrared light. In some embodiments, the one ormore sensors provide data on acoustic energy or mechanical energy thatis reflected, scattered, absorbed, transmitted, or emitted by theobject. In some embodiments, subtraction of a reference data set is usedto increase contrast between normal and defective features of theobject. In some embodiments, the one or more sensors provide data on anelectrical conductivity or a thermal conductivity of the object. In someembodiments, the machine learning algorithm comprises a supervisedlearning algorithm, an unsupervised learning algorithm, asemi-supervised learning algorithm, a reinforcement learning algorithm,a deep learning algorithm, or any combination thereof. In someembodiments, at least one of the one or more sensors provide data asinput to the machine learning algorithm at a rate of at least 100 Hz. Insome embodiments, the classification of detected object defects isadjusted at a rate of at least 100 Hz. In some embodiments, the objectdefects that are detected are classified using a support vector machine(SVM), artificial neural network (ANN), or decision tree-based expertlearning system. In some embodiments, the object defects are detected asdifferences between object property data and a reference data set thatare larger than a specified threshold, and are classified using aone-class support vector machine (SVM) or autoencoder algorithm. In someembodiments, the object defects are detected and classified using anunsupervised one-class support vector machine (SVM), autoencoder,clustering, or nearest neighbor (kNN) machine learning algorithm and atraining data set that comprises object property data for defective anddefect-free objects.

INCORPORATION BY REFERENCE

All publications, patents, and patent applications mentioned in thisspecification are herein incorporated by reference in their entirety tothe same extent as if each individual publication, patent, or patentapplication was specifically and individually indicated to beincorporated by reference in its entirety. In the event of a conflictbetween a term herein and a term in an incorporated reference, the termherein controls.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth with particularity inthe appended claims. A better understanding of the features andadvantages of the present invention will be obtained by reference to thefollowing detailed description that sets forth illustrative embodiments,in which the principles of the invention are utilized, and theaccompanying drawings of which:

FIG. 1 provides a schematic illustration of a machine learning-basedsystem for providing real-time adaptive control of free form depositionprocesses, e.g., additive manufacturing processes.

FIG. 2 is a schematic diagram of an example set-up for a materialdeposition process, e.g., a laser-metal wire deposition process,according to some embodiments of the present disclosure.

FIGS. 3A-C provide schematic illustrations of the conversion of a CADdesign for a three-dimensional object to a continuous, spiral wound“two-dimensional” layer (of finite thickness) and associated helicaltool path (FIG. 3A), or a stacked series of “two-dimensional” layers andassociated circular, layer-by-layer tool paths (FIG. 3B) for depositionof material using an additive manufacturing process. FIG. 3C:illustration of the tool path for a robotically manipulated depositiontool and simulation of the resulting object fabricated using an additivemanufacturing process.

FIGS. 4A-C provide examples of FEA simulation data for modeling of alaser-metal wire deposition melt pool. FIG. 4A: isometric view ofcolor-encoded three dimensional FEA simulation data for the liquidfraction of material in the melt pool being deposited by a laser-metalwire deposition process. FIG. 4B: cross-sectional view of the FEAsimulation data for the liquid fraction of material in the melt pool.FIG. 4C: cross-sectional view of color-encoded three dimensional FEAsimulation data for the static temperature of the material in the meltpool.

FIG. 5 is a diagram of one non-limiting example of a specific type ofadditive manufacturing system, i.e., a laser-metal wire depositionsystem.

FIGS. 6A-B illustrate one non-limiting example of in-process featuremonitoring using interferometry. FIG. 6A: schematic illustration oflaser beams used to probe the geometry of the wire feed and melt pooloverlaid with a photo of a laser-metal wire deposition process. FIG. 6B:cross-sectional profiles (i.e., height profiles across the width of thedeposition) of the wire feed (solid line; peak) and previously depositedlayer (solid line; shoulders) and resulting melt pool (dashed line). Thex-axis (width) dimension is plotted in arbitrary units. The y-axis(height) dimension is plotted in units of millimeters relative to afixed reference point below the deposition layer.

FIGS. 7A-C illustrate one non-limiting example of in-process featureextraction from images of a laser-metal wire deposition process obtainedusing a machine vision system. FIG. 7A: raw image stream obtained frommachine vision system. FIG. 7B: processed image after de-noising,filtering, and edge detection algorithms have been applied. FIG. 7C:processed image after application of a feature extraction algorithm.

FIG. 8 illustrates an action prediction—reward loop for a reinforcementlearning algorithm according to some embodiments of the presentdisclosure.

FIG. 9 illustrates reward function construction based on monitoring theactions that a human operator chooses during a manually-controlleddeposition process.

FIG. 10 provides a schematic illustration of an artificial neuralnetwork according to some embodiments of the present disclosure, andexamples of the input(s) and output(s) of a neural network used toprovide real-time, adaptive control of an additive manufacturingdeposition process.

FIG. 11 provides a schematic illustration of the functionality of a nodewithin a layer of an artificial neural network.

FIG. 12 provides a schematic illustration of an integrated systemcomprising an additive manufacturing deposition apparatus, machinevision systems and/or other process monitoring tools, process simulationtools, post-build inspection tools, and a processor for running amachine learning algorithm that utilizes data from the machine visionand/or process monitoring tools, the process simulation tools, thepost-build inspection tools, or any combination thereof, to providereal-time adaptive control of the deposition process.

FIG. 13 provides a schematic illustration of a distributed systemcomprising an additive manufacturing deposition apparatus, machinevision systems and/or other process monitoring tools, process simulationtools, post-build inspection tools, and a processor for running amachine learning algorithm that utilizes data from the machine visionand/or process monitoring tools, the process simulation tools, thepost-build inspection tools, or any combination thereof, to providereal-time adaptive control of the deposition process. In someembodiments, the different components or modules of the system may bephysically located in different workspaces and/or worksites, and may belinked via a local area network (LAN), an intranet, an extranet, or theinternet so that process data (e.g., training data, process simulationdata, process control data, and post-build inspection data) and processcontrol instructions may be shared and exchanged between the differentmodules.

FIG. 14 illustrates one non-limiting example of an unsupervised featureextraction and data compression process.

FIG. 15 illustrates the expected outcome for one non-limiting example ofan unsupervised machine learning process for classification of objectdefects.

FIGS. 16A-C provide an example of post-process image feature extractionand correlation with build-time actions. FIG. 16A: image of part afterbuild process has been completed. FIG. 16B: post-build inspection output(CT scan). FIG. 16C: the CT scan image of FIG. 16B after automatedfeature extraction; automated feature extraction allows one to correlatepart features with build-time actions.

DETAILED DESCRIPTION

Disclosed herein are methods for automated classification of objectdefects, for example, for objects fabricated using an additivemanufacturing process or welding process, where the methods comprise: a)providing a training data set, wherein the training data set comprisesfabrication process simulation data, fabrication processcharacterization data, in-process inspection data, post-build inspectiondata, or any combination thereof, for a plurality of object designgeometries that are the same as or different from the object; b)providing one or more sensors, wherein the one or more sensors providereal-time data for one or more object properties; c) providing aprocessor programmed to provide a classification of detected objectdefects using a machine learning algorithm that has been trained usingthe training data set of step (a), wherein the real-time data from theone or more sensors is provided as input to the machine learningalgorithm and allows the classification of detected object defects to beadjusted in real-time. Also disclosed are systems designed to performautomated classification of object defects.

Disclosed herein are methods for real-time adaptive control of anadditive manufacturing or welding process comprising: a) providing aninput design geometry for an object; b) providing a training data set,wherein the training data set comprises process simulation data, processcharacterization data, in-process inspection data, post-build inspectiondata, or any combination thereof, for a plurality of design geometriesthat are the same as or different from the input design geometry of step(a) or any portion thereof; c) providing a predicted optimalset/sequence of one or more process control parameters for fabricatingthe object, wherein the predicted optimal set of one or more processcontrol parameters are derived using a machine learning algorithm thathas been trained using the training data set of step (b); and d)performing the additive manufacturing or welding process to fabricatethe object, wherein real-time process characterization data is providedas input to the machine learning algorithm to adjust one or more processcontrol parameters in real-time. Also disclosed are systems designed toimplement these methods, as illustrated schematically in FIG. 1. Asindicated in FIG. 1, in some embodiments, the disclosed methods foradaptive, real-time control of additive manufacturing or weldingprocesses may be implemented using a distributed system, e.g., wheredifferent components or modules of the system are physically located indifferent workspaces, at different work sites, or in differentgeographical locations, and process simulation data, processcharacterization data, in-process inspection data, post-build inspectiondata, and/or adaptive process control instructions are shared andexchanged between locations by means of a telecommunications network orthe internet.

As used herein, the terms “deposition process” and “free form depositionprocess” may refer to any of a variety of liquid-to-solid free formdeposition processes, solid-to-solid free form deposition processes,additive manufacturing processes, welding processes, and the like. Insome embodiments, the disclosed methods and systems may be applied toany of a variety of additive manufacturing processes, including, but notlimited to, fused deposition modeling (FDM), selective laser sintering(SLS), or selective laser melting (SLM), as will be described in moredetail below. In some preferred embodiments, the additive manufacturingprocess may comprise a liquid-to-solid free form deposition process,e.g., a laser-metal wire deposition process, or a welding process, e.g.a laser welding process.

In some embodiments, process simulation data may be incorporated intothe training data set used by the machine learning algorithm thatenables automated classification of object defects, prediction ofoptimal sets or sequences of process control parameters, adjustment ofprocess control parameters in real-time, or any combination thereof. Forexample, process simulation tools such as finite element analysis (FEA)may be used to simulate the process for fabricating an object or aspecific portion thereof, e.g., a feature, from any of a variety offabrication materials as a function of a specified set of processcontrol parameters. In some embodiments, process simulation tools may beused to predict an optimal set or sequence of process control parametersfor fabricating a specified object or object feature.

In some embodiments, process characterization data may be incorporatedinto the training data set used by the machine learning algorithm thatenables automated classification of object defects, prediction ofoptimal sets or sequences of process control parameters, adjustment ofprocess control parameters in real-time, or any combination thereof. Forexample, process characterization data may be provided by any of avariety of sensors or machine vision systems, as will be described inmore detail below. In some embodiments, process characterization datamay be fed to the machine learning algorithm in order to update theprocess control parameters of an additive manufacturing apparatus inreal-time.

In some embodiments, in-process or post-build inspection data may beincorporated into the training data set used by the machine learningalgorithm that enables automated classification of object defects,prediction of optimal sets or sequences of process control parameters,adjustment of process control parameters in real-time, or anycombination thereof. For example, in-process or post-build inspectiondata may include data from visual or machine vision-based measurementsof object dimensions, surface finish, number of surface cracks or pores,etc., as will be described in more detail below. In some embodiments,in-process inspection data (e.g., automated defect classification data)may be used by the machine learning algorithm to determine a set orsequence of process control parameter adjustments that will implement acorrective action, e.g., to adjust a layer dimension or thickness, so asto correct the defect when first detected. In some embodiments,in-process inspection data (e.g., automated defect classification data)may be used by the machine learning algorithm to send a warning or errorsignal to an operator, or optionally, to automatically abort thedeposition process, e.g., an additive manufacturing process.

In some embodiments, the training data set is updated with additionalprocess simulation data, process characterization data, in-processinspection data, post-build inspection data, or any combination thereof,after each iteration of an additive manufacturing process that isperformed iteratively. In some embodiments, the training data setfurther comprises process characterization data, in-process inspectiondata, post-build inspection data, or any combination thereof, that isgenerated by a skilled operator while manually setting the input processcontrol parameters for an additive manufacturing process to produce aspecified set of objects or parts, or while manually adjusting theprocess control parameters in response to changes in process parametersor environmental variables to maintain a specified quality of theobjects or parts being produced. In some embodiments, the training dataset may comprise process simulation data, process characterization data,in-process inspection data, post-build inspection data, or anycombination thereof that is collected from a plurality of additivemanufacturing apparatus operating serially or in parallel.

A variety of different machine learning algorithms known to those ofskill in the art may be employed to implement the disclosed methods forautomated object defect classification and adaptive control of additivemanufacturing or welding processes. Examples include, but are notlimited to, artificial neural network algorithms, Gaussian processregression algorithms, fuzzy logic-based algorithms, decision treealgorithms, etc., as will be described in more detail below. In someembodiments, more than one machine learning algorithm may be employed.For example, automated classification of object defects may beimplemented using one type of machine learning algorithm, and adaptivereal-time process control may be implemented using a different type ofmachine learning algorithm. In some embodiments, hybrid machine learningalgorithms that comprise features and properties drawn from two, three,four, five, or more different types of machine learning algorithms maybe employed to implement the disclosed methods and systems.

In some embodiments, the disclosed methods for automated classificationof object defects and adaptive real-time control may be implementedusing components, e.g., additive manufacturing and/or welding apparatus,process control monitors or sensors, machine vision systems, and/orpost-build inspection tools, which are co-localized in a specificworkspace and which have been integrated to form stand-alone,self-contained systems. In some embodiments, the disclosed methods maybe implemented using modular components, e.g., additive manufacturingand/or welding apparatus, process control monitors or sensors, machinevision systems, and/or post-build inspection tools, that are distributedover different workspaces and/or different worksites, and that arelinked via a local area network (LAN), an intranet, an extranet, or theinternet so that process data (e.g., training data, process simulationdata, process control data, and post-build inspection data) and processcontrol instructions may be shared and exchanged between the differentmodules. In some embodiments, a plurality of additive manufacturingand/or welding apparatus are linked to the same distributed system sothat process data is shared amongst two or more additive manufacturingand/or welding apparatus control systems, and used to update thetraining data set for the entire distributed system.

The disclosed methods and systems for automated object defectclassification and adaptive real-time control of additive manufacturingand/or welding apparatus may provide for rapid optimization andadjustment of the process control parameters used in response to changesin process or environmental parameters, as well as improved processyield, process throughput, and quality of the parts that are produced.The methods and systems are applicable to parts fabrication in a varietyof different technical fields and industries including, but not limitedto, the automotive industry, the aeronautics industry, the medicaldevice industry, the consumer electronics industry, etc.

Definitions

Unless otherwise defined, all technical terms used herein have the samemeaning as commonly understood by one of ordinary skill in the art inthe field to which this disclosure belongs. As used in thisspecification and the appended claims, the singular forms “a”, “an”, and“the” include plural references unless the context clearly dictatesotherwise. Any reference to “or” herein is intended to encompass“and/or” unless otherwise stated.

As used herein, the term “free form deposition process” may refer to anyof a variety of liquid-to-solid free form deposition processes,solid-to-solid free form deposition processes, additive manufacturingprocesses, welding processes, and the like.

As used herein, the term “joining process” may refer to any of a varietyof welding processes.

As used herein, the term “data stream” refers to a continuous ordiscontinuous series or sequence of analog or digitally-encoded signals(e.g., voltage signals, current signals, image data comprisingspatially-encoded light intensity and/or wavelength data, etc.) used totransmit or receive information.

As used herein, the term “process window” refers to a range of processcontrol parameter values for which a specific manufacturing processyields a defined result. In some instances, a process window may beillustrated by a graph of process output plotted as a function ofmultiple process control parameters, with a central region indicatingthe range of parameter values for which the process behaves well, andouter borders that define regions where the process becomes unstable orreturns an unfavorable result.

As used herein, the term “machine learning” refers to any of a varietyof artificial intelligence or software algorithms used to performsupervised learning, unsupervised learning, reinforcement learning, orany combination thereof.

As used herein, the term “real-time” refers to the rate at which sensordata is acquired, processed, and/or used in a feedback loop with amachine learning algorithm to update a classification of object defectsor to update a set or sequence of process control parameters in responseto changes in one or more input process data streams comprising processsimulation data, process characterization data, in-process inspectiondata, post-build inspection data, or any combination thereof.

Additive Manufacturing Processes:

The term “additive manufacturing” refers to a collection of versatilefabrication techniques for rapid prototyping and manufacturing of partsthat allow 3D digital models (CAD designs) to be converted to threedimensional objects by depositing multiple thin layers of materialaccording to a series of two dimensional, cross-sectional depositionmaps. Additive manufacturing may also be referred to as “direct digitalmanufacturing”, “solid free form fabrication”, “liquid solid free formfabrication”, or “3D printing”, and may comprise deposition of materialin a variety of different states including liquid, powder, and as fusedmaterial. A wide variety of materials can be processed using additivemanufacturing methods, including metals, alloys, ceramics, polymers,composites, airy structures, and multi-phase materials. One of the mainadvantages of additive manufacturing processes is the reduced number offabrication steps required to transform a virtual design into aready-to-use (or nearly ready-to-use) part. Another major advantage isthe ability to process complex shapes that are not easy to fabricateusing conventional machining, extrusion, or molding techniques.

Specific examples of additive manufacturing techniques to which thedisclosed object defect classification and adaptive process controlmethods may be applied include, but are not limited to,stereolithography (SLA), digital light processing (DLP), fuseddeposition modeling (FDM), selective laser sintering (SLS), selectivelaser melting (SLM), or electronic beam melting (EBM) processes.

Stereolithography (SLA): In stereolithography, a tank of liquidultraviolet curable resin is used in combination with a scanned laserbeam to cure one thin layer of resin at a time according to atwo-dimensional exposure pattern. When one layer is done, the bed orbase that it was cured on is lowered slightly into the tank and anotherlayer is cured. The build platform repeats the cycle of layer curing anddownward steps until the part is complete. The amount of time requiredfor each cycle of the process depends on the cross-sectional area of thepart and the spatial resolution required. By the time that the part iscomplete, it is completely submerged in the uncured resin. It is thenpulled from the tank and may optionally be further cured in anultraviolet oven.

Digital light processing (DLP): Digital light processing is a variationof stereolithography in which a vat of liquid polymer is exposed tolight from a DLP projector (e.g., which uses one or more digitalmicromirror array devices) under safelight conditions. The DLP projectorprojects the image of a 3D model onto the liquid polymer. The exposedliquid polymer hardens and the build plate moves down and the liquidpolymer is once more exposed to light. The process is repeated until the3D object is complete and the vat is drained of liquid, revealing thesolidified model. DLP 3D printing is fast and may print objects with ahigher resolution than some other techniques.

Fused deposition modeling (FDM): Fused deposition modeling is one of themost common forms of 3D printing, and is sometimes also called FusedFilament Fabrication (FFF). FDM printers can print in a variety ofplastics or polymers, and typically print with a support material. FDMprinters use extruder heads that super heat the input plastic filamentso that it becomes a liquid, and then push the material out in a thinlayer to slowly fabricate an object in a layer-by-layer process.

Selective laser sintering (SLS): Selective uses a laser to fuse materialtogether layer by layer. A layer of powder is pushed onto the buildplatform and heated by a laser (and sometimes also compressed) so thatit fuses without passing through a liquid state. Once that is done,another layer of powder is applied and heated again. The processrequires no support material as the leftover material holds it upright.After the part is complete, one removes it from the powder bed and cleanoff any excess material.

Selective laser melting (SLM): Selective laser melting is a variation ofselective laser sintering and direct metal laser sintering (DMLS) (Yap,et al. (2015), “Review of Selective Laser Melting: Materials andApplications”, Applied Physics Reviews 2:041101). A high power laser isused to melt and fuse metallic powders. A part is built by selectivelymelting and fusing powders within and between layers. The technique is adirect write technique, and has been proven to produce near net-shapeparts (i.e., fabricated parts that are very close to the final (net)shape, thereby reducing the need for surface finishing and greatlyreducing production costs) with up to 99.9% relative density. Thisenables the process to build near full density functional parts. Recentdevelopments in the fields of fiber optics and high-powered lasers havealso enabled SLM to process different metallic materials, such ascopper, aluminium, and tungsten, and has opened up researchopportunities in SLM of ceramic and composite materials.

Electronic beam melting (EBM): Electron beam melting is an additivemanufacturing technique, similar to selective laser melting. EBMtechnology fabricates parts by melting metal powder layer by layer withan electron beam under high vacuum. In contrast to sintering techniques,both EBM and SLM achieve full melting of the metal powder. This powderbed method produces fully dense metal parts directly from a metal powderwhich have the characteristics of the target material. The EBMdeposition apparatus reads data from a 3D CAD model and lays downsuccessive layers of powdered material. These layers are melted togetherutilizing a computer controlled electron beam to build parts layer bylayer. The process takes place under vacuum, which makes it suitable forthe manufacture of parts using reactive materials with a high affinityfor oxygen, e.g., titanium. The process operates at higher temperaturesthan many other techniques (up to 1000° C.), which can lead todifferences in phase formation though solidification and solid statephase transformation. The powder feedstock is typically pre-alloyed, asopposed to being a mixture. Compared to SLM and DMLS, EBM generally hasa faster build rate because of its higher energy density and scanningmethod.

Laser-Metal Wire Deposition:

In one preferred embodiment, the additive manufacturing processes andsystems to which the disclosed defect classification and adaptivecontrol methods may be applied is laser-metal wire deposition. Thecentral process in laser-metal wire deposition is the generation ofbeads of deposited material (a plurality of which may be required toform a single layer) using a high power laser source and additivematerial in the form of metal wire (Heralić (2012), Monitoring andControl of Robotized Laser Metal-Wire Deposition, Ph.D. Thesis,Department of Signals and Systems, Chalmers University of Technology,Göteborg, Sweden). The laser generates a melt pool on the substratematerial, into which the metal wire is fed and melted, forming ametallurgical bound with the substrate. By moving the laser processinghead and the wire feeder, i.e., the deposition (or welding) tool,relative to the substrate a bead is formed during solidification. Therelative motion of the deposition tool and the substrate may becontrolled, for example, using a 6-axis industrial robot arm. Theformation of a deposited layer is illustrated in FIG. 2, as will bedescribed in more detail below.

Prior to beginning deposition, a set of process parameters typicallyneeds to be chosen and the equipment needs to be adjusted accordingly.Important process control parameters for laser-metal wire depositioninclude the laser power setting, the wire feed rate, and the traversespeed. These control the energy input, the deposition rate and thecross-section profile of the layer being deposited, i.e., the width andthe height of the layer. The height (or thickness) of the depositedlayer is determined by the amount of wire that is fed into the melt poolin relation to the traverse speed and the laser power. Once the nominallaser power, traverse speed, and the wire feed rate have been specified,there may be additional parameters to set, e.g., the relativeorientation of the wire feed to the laser beam and substrate for a giventraverse speed. Careful adjustment of these parameters is necessary inorder to attain stable deposition on a flat surface.

Examples of the process control parameters that may need to beconsidered in order to achieve stable deposition of uniform beads ofmaterial on a flat surface include, but are not limited to:

Laser power: one of the main process control parameters, the laser powersetting determines the maximum energy input. Depending on the laser beamsize and the traverse speed, laser power also controls the melt poolsize and consequently the width of the deposited bead.

Laser power distribution: affects the melt pool dynamics. Non-limitingexamples of different laser power (or beam profile) distributionsinclude step-function and Gaussian distributions.

Laser/wire or laser/substrate angle: affect the process window and thetrue energy input. The angle between the laser beam and the wire feedimpacts the sensitivity of the deposition process to changes in wirefeed rate and variations in distance between the wire nozzle and thesubstrate. The angle between the laser beam and the substrate impactsthe reflection of the laser beam from the substrate surface, and hencethe amount of absorbed energy.

Laser beam size and shape: control the size and the shape of the meltpool (together with the laser power and the traverse speed). The use ofa circular beam shape is common, although rectangular shapes are beingused as well (e.g., with diode lasers). The size is chosen to reflectthe desired bead width.

Laser beam focal length: controls how collimated the laser beam is atthe substrate surface. Consequently, it impacts the sensitivity of thedeposition process to distance variations between the focus lens and thesubstrate.

Laser wavelength: controls the absorbance of the laser beam by thedeposited material. For metals, the absorbance of laser light varieswith wavelength (and specific materials).

Wire feed rate: another one of the main process control parameters, thewire feed rate impacts the amount of mass deposited per unit time. Thewire feed rate primarily impacts the bead height, and needs to be chosenin relation to the laser power and the traverse speed.

Wire diameter: should be chosen in relation to the laser beam size toensure proper melting and a flexible process.

Wire/substrate angle: affects the melting of the wire and thereby alsothe stability of the deposition process. Under proper conditions, thetransfer of metal between the wire and the melt pool is smooth andcontinuous. Use of an improper wire/substrate angle may cause the metaltransfer process to result in either globular deposition, e.g., as aseries of droplets on the substrate surface, or the wire may still besolid as it enters the melt pool. Use of a higher angle reducessensitivity to deposition direction, but at the same time results in asmaller process window of allowable wire feed rates.

Wire tip position relative to the melt pool: also affects the meltingrate of the wire and thereby the stability of the process.

Wire stick-out: typically not as critical as the wire angle or the wiretip position, but the stick-out distance may need to be adjusteddepending on the expected deposition conditions. It primarily affectsthe sensitivity of the process to variations in height between the wirenozzle and the substrate.

Shield gas: use of a shield gas may impact the degree to whichcontaminants and/or defects are introduced into the deposition layer.Composition, flow rate, and/or angle of incidence may be adjusted insome embodiments.

Feed direction: determines from which direction the wire enters the meltpool, and thereby affects the melting of the wire, and thus the metaltransfer process. Different choices of feed direction change the rangeof allowed wire feed rates that may be used. In some cases it can alsoaffect the shape of the deposited bead.

Traverse speed: another one of the main process control parameters, thetraverse speed impacts the amount of material deposited per unit lengthand the input energy per unit length. At lower traverse speeds thedeposition process is typically more stable, unless the temperature ofthe deposited material becomes too high. At high traverse speeds, lowerenergy inputs can be obtained for the same amount of material depositedper unit length. However, the motion control system's acceleration andpath accuracy become more critical.

Process stability: Proper tuning of the process control parametersdescribed above influences the rate of transfer of metal between thesolid wire and the melt pool, which is important for the stability ofthe deposition process. In general, there are three ways that the metalwire can be deposited: by globular (droplet-like) transfer, smoothtransfer, or by plunging (i.e., incomplete melting of the wire prior toentering the melt pool). Only smooth transfer results in a stabledeposition process.

If the deposition apparatus is set-up so that the wire tip spends toomuch time in the laser beam (e.g., by choosing a feed angle that is toohigh in relation to the other process control parameters), it will reachthe melting temperature somewhere prior to entering the melt pool. Thetransfer of metal between the solid wire and the melt pool might then bestretched to a point where surface tension can no longer maintain theflow of metal, resulting in the formation and separation of surfacetension-induced spherical droplets. This type of deposition gives riseto highly irregular bead shapes and a poor deposition process. Onceglobular transfer starts, it is typically hard to abort. The physicalcontact between the molten wire tip and the melt pool must bere-established, and the process control parameters must be adjusted toappropriate values.

Alternatively, if the wire feed angle is carefully adjusted so that thewire is melted close to the intersection with the melt pool, there willbe a smooth transfer of metal from the solid wire to the liquid metal ofthe melt pool. The resulting beads of deposited metal will have a smoothsurface and a stable metallurgical bond to the substrate.

Another way to melt the wire is by heat conduction from the melt pool,i.e., by plunging the wire into the melt pool. Precautions must be takento adjust the wire feed rate to a value sufficiently low relative to themelting rate provided by the heat energy in the melt pool that the wiremelts completely. Incomplete melting can result in, for example, lack offusion (LOF) defects. Note that LOF defects may occur even at low wirefeed rates for which the resulting beads are more or lessindistinguishable from normal bead depositions.

Adjustment of process parameters: The process control parametersdescribed above are adjusted depending on the choice of material and theenergy input required to melt the material, which in turn is determinedbased on the desired deposition rate, deformation restrictions, thematerial's viscosity, and the available laser power and beam spot sizes.These factors put a requirement on the laser power, the traverse speed,and the wire feed rate settings. The laser beam should preferably be asorthogonal to the melt pool as possible to minimize reflection whileavoiding back reflection into the optical system. The wire tip positionrelative to the melt pool should be adjusted with regard to the chosenamount of material deposited per time unit. If a front feedconfiguration is used and the deposition rate is low, the wire shouldenter the melt pool closer to the leading edge. Changing this parametermainly affects the maximum and minimum wire feed rate for the chosenlaser power and traverse speed. A closely related parameter to the wiretip position is the wire/substrate angle. If the angle is low, high wirefeed rates might be possible since plunging can be exploited in a betterway. However, for extreme wire feed rates, only front feeding isfeasible. This then limits the choice of complex deposition paths, suchas zig-zag or spiral patterns. To decrease the sensitivity of thedeposition process to feed direction and thereby allow for arbitrarydeposition patterns, the angle between the wire and the substrate shouldbe increased. However, increased flexibility in terms of allowabledeposition patterns is often achieved at the cost of a smaller processwindow.

Multi-layered deposition: Obtaining stable deposition of a single beadof material on a flat substrate requires careful adjustment of theprocess control parameters, as discussed above. Ultimately, however, thegoal is to deposit three-dimensional parts, i.e., to deposit severaladjacent beads in a layer, and to repeat the deposition for a number oflayers. The transition from deposition of a single bead to deposition ofa three-dimensional part is often not straightforward. The precise shapeof the individual layers are influenced by several additional factors,e.g., the deposition pattern, the distance between adjacent beads, andthe motion control system's speed and path accuracy. The relationshipbetween these factors and their impact of the resulting layer arecomplex and hard to predict, which complicates the adjustment of processcontrol parameters required to achieve a given deposition designfeature, e.g., the layer height. Another example of a factor thatcomplicates the deposition of three-dimensional parts is the potentialincrease in local temperature of the part due to heat accumulation,which needs to be considered during multi-layered deposition. Heat maybe accumulated in the deposited part, for example, due to the use ofoverly short pauses between deposition of adjacent layers.

The additional uncertainties that arise in three-dimensional depositionmay create a problem from a process stability point of view. Forexample, if the estimate of layer height to be achieved is incorrect,the relationship between the wire tip and the substrate will bedifferent from what was expected for the process parameters asoriginally set. As a result, the deposition process might transitionfrom a smooth transfer of the molten wire to either a globulardeposition mode or a wire plunging mode. Consequently, as long as thedeposition process is not sufficiently understood and/or tightlycontrolled that the dimensions of the individual layers can beaccurately predicted, three-dimensional deposition may requirecontinuous on-line monitoring and/or process control parameteradjustment.

Difficulties in Optimizing Additive Manufacturing Processes:

Some of the difficulties discussed above in the context of laser-metalwire deposition are also applicable to other additive manufacturingprocesses (Guessasma, et al., (2015) “Challenges of AdditiveManufacturing Technologies from an Optimisation Perspective”, Int. J.Simul. Multisci. Des. Optim. 6, A9). Generation of the toolpaths fromthree-dimensional CAD models represents the first challenge. Mostadditive manufacturing technologies rely on a successive layer-by-layerfabrication process, so starting from a three-dimensional representationof the part (i.e., a tessellated version of the part's actual surface)and ending with a two-dimensional build strategy may introduce errors.The problem is particularly prevalent in droplet-based 3D printingapproaches, as discontinuities in the fused material may appear in allbuild directions as a result of the layer-by-layer deposition process,and may lead to dimensional inaccuracy, unacceptable finish state, andstructural and mechanical anisotropies. Anisotropy may also arise in thedevelopment of particular grain texture, for example, in laser meltingdeposition or arc welding of metals. Reduction of anisotropy maysometimes be achieved by selecting the appropriate build orientation ofthe virtual design.

In addition, the differences between a virtual design and theas-fabricated object may sometimes be significant due to the finitespatial resolution available with the additive manufacturing toolingused, or due to part shrinkage during solidification of the depositedmaterial, which can cause both changes in dimension as well asdeformation of the part. Consider, for example, fused depositionmodelling for which the toolpath comprises a collection of filamentpaths of finite dimension. This has three main consequences on thefabricated object: (i) internal structural features may not be wellcaptured depending on their size; (ii) discontinuities may appeardepending on local curvature; and (iii) the surface finish state may belimited due to rough profiles arising from the fusing of multiplefilaments.

One consequence of the discontinuous fabrication process and otherissues related to additive manufacturing process errors is porosity.Many technical publications have been directed to the evaluation of theeffect of porosity in printed parts. One particular consequence is thatporosity may reduce the mechanical performance of the part, e.g.,through a decrease of stiffness with increased porosity level, orthrough lower mechanical strength under tension because of thedevelopment of porosity-enhanced damage in the form of micro-cracks. Itshould be noted that porosity may not always be viewed as a negativeconsequence of additive manufacturing processes, as it can be used, forexample, to increase permeability in some applications.

Another type of defect encountered with some additive manufacturingprocesses is the presence of support material trapped between internalsurfaces. Support material is sometimes needed to reinforce fragileprinted structures during the printing process. Although these materialsare typically selected to exhibit limited adhesion to the depositedmaterials, incomplete removal resulting in residual amounts of supportmaterial in the part may contribute to, for example, increased weight ofthe part and a modified load bearing distribution, which in turn mayalter the performance of the part relative to that expected based on theoriginal design. In addition, non-optimized support deposition mayaffect the finish state of the part, material consumption, fabricationtime, etc. Various strategies have been described in the literature toreduce the dependence of additive manufacturing processes on the use ofsupport materials. The strategies may vary depending on the geometry ofthe part and the choice of material to be deposited.

Welding Processes:

In some embodiments, the disclosed defect classification and processcontrol methods and systems may be applied to welding processes andapparatus instead of, or in combination with, additive manufacturingprocesses and apparatus. Examples of welding processes and apparatusthat may be employed with the disclosed process control methods andsystems include, but are not limited to, laser beam welding processesand apparatus, MIG (metal inert gas) welding processes and apparatus(also referred to as gas metal arc welding), TIG (tungsten inert gas)welding processes and apparatus, and the like.

Laser beam welding (LBW): a welding technique used to join metalcomponents that need to be joined with high welding speeds, thin andsmall weld seams and low thermal distortion. The laser beam provides afocused heat source, allowing for narrow, deep welds and high weldingrates. The high welding speeds, automated operation, and capability toimplement feedback control of weld quality during the process make laserwelding a common joining method in modern industrial production.Examples of automated, high volume applications include use in theautomotive industry for welding car bodies. Other applications includethe welding of fine, non-porous seams in medical technology, precisionspot welding in the electronics or jewelry industries, and welding intool and mold-making.

MIG welding: an arc welding process in which a continuous solid wireelectrode is fed through a welding gun and into the weld pool, joiningthe two base materials together. A shielding gas is also sent throughthe welding gun and protects the weld pool from contamination, hence thename “metal inert gas” (MIG) welding. MIG welding is typically used tojoin thin to medium thick sheets of metal.

TIG welding: TIG welding (technically called gas tungsten arc welding(GTAW)) is a process that uses a non-consumable tungsten electrode todeliver the current to the welding arc. The tungsten and weld puddle areprotected and cooled with an inert gas, typically argon. TIG weldingtypically produces a somewhat neater and more controlled weld than MIGwelding.

Conversion of 3D CAD Files to Layers and Tool Paths:

Computer-aided design: The first step in a typical free form depositionprocess, such as an additive manufacturing process, is to create a threedimensional model of the object to be fabricated using a computer-aideddesign (CAD) software package. Any of a variety ofcommercially-available CAD software packages may be used including, butnot limited to, SolidWorks (Dassault Systèmes SolidWorks Corporation,Waltham, Mass.), Autodesk Fusion 360 (Autodesk, Inc., San Rafael,Calif.), Autodesk Inventor (Autodesk, Inc., San Rafael, Calif.), PTCCreo Parametric (Needham, Mass.), and the like.

Conversion to STL file format: Once the CAD model is completed, it istypically converted to the standard STL (stereolithography) file format(also known as the “standard triangle language” or “standardtessellation language” file format) that was originally developed by 3DSystems (Rock Hill, S.C.). This file format is supported by many othersoftware packages and is widely used for rapid prototyping, 3D printing,and computer-aided manufacturing. STL files describe only the surfacegeometry of a three-dimensional object without any representation ofcolor, texture or other common CAD model attributes. In an ASCII STLfile, the CAD model is represented using triangular facets, which aredescribed by the x-, y-, and z-coordinates of the three vertices(ordered according to the right-hand rule) and a unit vector to indicatethe normal direction that points outside of the facet (Ding, et al.(2016), “Advanced Design for Additive Manufacturing: 3D Slicing and 2DPath Planning”, Chapter 1 in New Trends in 3D Printing, I. Shishkovsky,Ed., Intech Open).

Slicing the STL model to create layers: Once the STL file has beencreated unidirectional or multidirectional slicing algorithms are usedto slice the STL model into a series of layers according to the builddirection. Uniform slicing methods create layers having a constantthickness. The accuracy of additively manufactured parts may sometimesbe improved by altering the layer thickness. Typically, the smaller thelayer thickness, the higher the achieved accuracy will be. The materialdeposition rate is also highly relevant to the sliced layer thickness.Adaptive slicing approaches thus slice the STL model with a variablethickness. Based on the surface geometry of the model, this approachautomatically adjusts the layer thickness to improve the accuracy of thefabricated part or to improve the build time.

As noted above, many additive manufacturing processes utilize slicing a3D CAD model into a set of two-dimensional layers having either aconstant or adaptive thickness, where the layers are stacked in a singlebuild direction. However, when fabricating parts with complex shapesunidirectional slicing strategies are generally limited by the need toinclude support structures for fabrication of overhanging features. Theneed to deposit support structures results in longer build times,increased material waste, and increased (and sometimes costly)post-processing for the removal of the supports. Some additivemanufacturing techniques are capable of depositing material alongmultiple build directions. The use of multi-directional deposition helpsto eliminate or significantly decrease the requirement for supportstructures in the fabrication of complex objects. A key challenge inmulti-directional additive manufacturing is to develop robust algorithmscapable of automatically slicing any 3D model into a set of layers whichsatisfy the requirements of support-less and collision-free layereddeposition. A number of strategies for achieving this have beendescribed in the technical literature (Ding, et al. (2016), “AdvancedDesign for Additive Manufacturing: 3D Slicing and 2D Path Planning”,Chapter 1 in New Trends in 3D Printing, I. Shishkovsky, Ed., IntechOpen).

Tool path planning: Another important step in free form deposition oradditive manufacturing is the development of tool path strategies basedon the layers identified by the slicing algorithm. Tool path planningfor powder-based additive manufacturing processes that utilize fine,statistically-distributed particles is somewhat independent of geometriccomplexity. However, tool path planning for additive manufacturingprocesses that utilize larger, sometimes coarse beads of depositedmaterial may be directly influenced by geometric complexity. Inaddition, the properties of the deposited material (height and width ofthe bead, surface finish, etc.) may be influenced by the deposition toolpath trajectory. A variety of tool path planning strategies have beendescribed in the technical literature including, but not limited to, theuse of raster tool paths, zigzag tool paths, contour tool paths, toolpaths, hybrid tool paths, continuous tool paths, hybrid and continuoustool paths, medial axis transformation (MAT) tool paths, and adaptiveMAT tool paths.

Raster tool paths: The raster scanning tool path technique is based onplanar ray casting along one direction. Using this tool path approach,two-dimensional regions of a given layer are filled in by depositing aset of material beads having finite width. Commonly employed incommercial additive manufacturing systems, it features simpleimplementation and is suitable for use with almost any arbitraryboundary.

Zigzag tool paths: Derived from the raster approach, zigzag tool pathgeneration is the most popular method used in commercial additivemanufacturing systems. Compared to the raster approach, the zigzagapproach significantly reduces the number of tool path passes (and hencethe build time) required to fill in the geometry line-by-line bycombining the separate parallel lines into a single continuous zigzagpass. As with the raster tool path approach, the outline accuracy of thepart is sometimes poor due to discretization errors on any edge that isnot parallel to the tool motion direction.

Contour tool paths: Contour tool paths, another frequently used toolpath method, help address the geometrical outline accuracy issue notedabove by following the part's boundary contours. Various contour mappatterns have been described in the literature for developing optimaltool path patterns for parts comprising primarily convex shapes that mayalso include openings or ‘islands” (isolated sections of a model withina given layer).

Spiral tool paths: Spiral tool paths have been widely applied incomputer numerically controlled (CNC) machining, e.g., fortwo-dimensional pocket milling (i.e., removal of material inside of anarbitrarily closed boundary on a flat surface of a work piece to aspecified depth). This method can also be used with additivemanufacturing processes to overcome the boundary problems of zigzag toolpaths, but is typically only suitable for certain special geometricalmodels.

Hybrid tool paths: Hybrid tool paths share some of the features of morethan one approach. For example, a combination of contour and zigzag toolpath patterns is sometimes developed to meet both the geometricalaccuracy requirements of a part and to improve the overall buildefficiency.

Continuous tool paths: The goal of continuous tool path approaches is tofill in a deposition layer using one continuous path, i.e., a tool paththat is capable of filling in an entire region without intersectingitself. This approach has been found to be particularly useful inreducing shrinkage during some additive manufacturing fabricationprocesses. However, the approach often necessitates frequent changes inpath direction that may not be suitable for some deposition processes.Furthermore, when the area to be filled is large and the accuracyrequirement is high, the processing time required may be unacceptablylong. In addition, highly convoluted tool paths may result in excessaccumulation of heat in certain regions of the part, thereby inducingunacceptable distortion of the part.

Hybrid continuous tool paths: Tool path strategies have been developedwhich combine the merits of zigzag and continuous tool path patterns. Inthese approaches, the two-dimensional geometry is first decomposed intoa set of monotone polygons. For each monotone polygon, a closed zigzagcurve is then generated. Finally, a set of closed zigzag curves arecombined together into an integrated continuous tortuous path. Recently,another continuous path pattern which combines the advantages of zigzag,contour, and continuous tool path patterns has been developed.

Medial axis transformation (MAT) tool paths: An alternative methodologyfor generating tool paths uses the medial axis transformation (MAT) ofthe part geometry to generate offset curves by starting at the insideand working toward the outside, instead of starting from the layerboundary and filling toward the inside. The medial axis of an object isthe set of all points having more than one closest point on the object'sboundary. In two dimensions, for example, the medial axis of a subset Sof circles which are bounded by planar curve C is the locus of thecenters of all circles within S that tangentially intersect with curve Cat two or more points. The medial axis of a simple polygon is atree-like skeleton whose branches are the vertices of the polygon. Themedial axis together with an associated radius function of maximallyinscribed circles is called the medial axis transform (MAT). The medialaxis transform is a complete shape descriptor that can be used toreconstruct the shape of the original domain.

This approach is useful for computing tool paths which can entirely fillthe interior region of the layer geometry, and avoids producing gaps bydepositing excess material outside the boundary which can subsequentlybe removed through post-processing. Traditional contour tool pathpatterns which run from outside to inside are often used for machining,whereas MAT tool paths starting from the inside and working toward theoutside are often more suitable for additive manufacture of void-freeparts. The main steps for generating MAT-based tool paths are: (i)computation of the medial axis; (ii) decomposition of the geometry intoone or more regions or domains, where each domain is bounded by aportion of the medial axis and a boundary loop; (iii) generation of thetool path for each domain by offsetting from the medial axis loop towardthe corresponding boundary loop with an appropriate step-over distance.The offsetting is repeated until the domain is fully covered; and (iv)repeating step (iii) for each domain to generate a set of closed-looppaths, preferably without start/stop sequences. MAT path planning isfrequently used, for example, with arc welding systems, and isparticularly preferred for void-free additive manufacturing.

Adaptive MAT tool paths: Traditional contour tool paths frequentlygenerate gaps or voids. MAT tool path planning was introduced to avoidgeneration of internal voids during deposition, and has been extended tohandle complex geometries. As noted above, MAT tool paths are generatedby offsetting the medial axis of the geometry from the center toward thelayer boundary. Although MAT tool paths reduce the occurrence ofinternal voids, this is achieved at the cost of creating pathdiscontinuities and extra material deposition at the layer boundary.Post-process machining to remove the extra materials and improve thedimensional accuracy of the part requires extra time and adds to thecost. For both traditional contour tool paths and MAT tool paths, thestep-over distance, i.e., the distance between the next deposition pathand the previous deposition path, is held constant. For some partgeometries, it is not possible to achieve both high dimensional accuracyand void-free deposition using tool paths with constant step-overdistance. However, some additive manufacturing processes, such as wirefeed additive manufacturing processes, are capable of producingdifferent deposited bead widths within a layer by varying processcontrol parameters like travel speed and wire feed rate, whilemaintaining constant deposition height. Adaptive MAT tool path planninguses continuously varying step-over distances by adjusting the processparameters to deposit beads with variable width within a given toolpath. Adaptive MAT path planning algorithms are able to automaticallygenerate path patterns with varying step-over distances by analyzing thepart geometry to achieve better part quality (void-free deposition),accuracy at the boundary, and efficient use of material.

Tool path generation software: Examples of toolpath generation softwareinclude Repetier (Hot-World, GmbH, Germany) and CatalystEx (StratasysInc. Eden Prairie Minn., USA).

FIGS. 3A-C provide schematic illustrations of the conversion of a CADdesign for a three-dimensional object to a continuous, spiral wound“two-dimensional” layer (of finite thickness) and associated helicaltool path (FIG. 3A), or a stacked series of “two-dimensional” layers andassociated circular, layer-by-layer tool paths (FIG. 3B) for depositionof material using an additive manufacturing process. FIG. 3C provides anillustration of the tool path for a robotically manipulated depositiontool and a simulation of the resulting object fabricated using anadditive manufacturing process. Tool path and part simulation using asoftware package such as Octopuz (Jupiter, Fla.) is performed beforerunning the deposition process on an actual deposition system. In someinstances, the predicted optimal tool path may be locally modifiedduring the deposition process in response to closed-loop feedbackcontrol. In some instances, the tool path may be reconstructed based onthe as-built part geometry after the deposition process is complete.

Process Simulation Tools:

In some embodiments of the disclosed adaptive process control methodsand systems, process simulation tools may be used to simulate the freeform deposition process (or joining process) and/or to provide estimatesof optimal sets (and/or sequences) of process control parameter settings(and adjustments). Any of a variety of process simulation tools known tothose of skill in the art may be used including, but not limited tofinite element analysis (FEA), finite volume analysis (FVA), finitedifference analysis (FDA), computational fluid dynamics (CFD), and thelike, or any combination thereof. In some embodiments of the disclosedmethods and system, process simulation data from past fabrication runsis used as part of a training data set used to “teach” the machinelearning algorithm used to run the process control.

Finite element analysis (FEA): Finite element analysis (also referred toas the finite element method (FEM)) is a numerical method for solvingengineering and mathematical physics problems, e.g., for use instructural analysis, or studies of heat transfer, fluid flow, masstransport, and electromagnetic potential. Analytical solution of thesetypes of problems generally requires the solution to boundary valueproblems involving partial differential equations, which may or may notsolvable. The computerized finite element approach allows one toformulate the problem as a system of algebraic equations, the solutionfor which yields approximate values of the unknown parameters at adiscrete number of points over the geometry or domain of interest. Theproblem to be solved is subdivided (discretized) into smaller, simplercomponents (i.e., the finite elements) to simplify the equationsgoverning the behavior of the system. The relatively simple equationsthat model the individual finite elements are then assembled into alarger system of equations that models the entire problem. Numericalmethods drawn from the calculus of variations are used to approximate asolution to the system of equations by minimizing an associated errorfunction. FEA is often used for predicting how a product will react whensubjected to real-world forces, e.g., stress (force per unit are or perunit length), vibration, heat, fluid flow, or other physical effects.

As noted above, in some embodiments of the disclosed adaptive processcontrol methods, FEA may be used to simulate a deposition process and/orto provide estimates of optimal sets and/or sequences of process controlparameter settings and adjustments thereof. Examples of depositionprocess parameters that may be estimated using FEA analysis (or othersimulation techniques) include, but are not limited to, a prediction ofa bulk or peak temperature of a deposited material, a cooling rate of adeposited material, a chemical composition of a deposited material, asegregation state of constituents in a deposited material, a geometricalproperty of a deposited material, an angle of overhang in a depositedgeometry, an intensity of heat flux out of a material during deposition,an electromagnetic emission from a deposition material, an acousticemission from a deposition material, or any combination thereof, as afunction of a set of specified input process control parameters. Becausethe process control parameters used as input for the calculation may beadjusted to determine how they impact the simulated deposition process,iterative use of process simulation may be used to provide estimates ofoptimal sets and/or sequences of process control parameter settings andadjustments thereof.

Finite volume analysis (FVA): Finite volume analysis (also referred toas the finite volume method (FVM)) is another numerical techniquerelated to finite element analysis that is used for solving partialdifferential equations, especially those that arise from physicalconservation laws. FVM uses a volume integral formulation of the problemwith a finite set of partitioning volumes to discretize the equationsrepresenting the original problem. FVA is, for example, commonly usedfor discretizing computational fluid dynamics equations.

Finite difference analysis (FDA): Finite difference analysis (alsoreferred to as the finite difference method (FDM)) is another numericalmethod for solving differential equations by approximating them withdifference equations, in which finite differences approximate thederivatives.

Computational fluid dynamics (CFD): Computational fluid dynamics refersto the use of applied mathematics, physics, and computational software(e.g. finite volume analysis software) to visualize how a gas or liquidflows in response to applied pressure, or to visualize how the gas orliquid affects objects as it flows past. Computational fluid dynamics isbased on solution of Navier-Stokes equations, which describe how thevelocity, pressure, temperature, and density of a moving fluid arerelated. CFD-based analysis is used in a variety of industries andapplications, for example, computational fluid dynamics has been used tomodel predictive control for controlling melt temperature in plasticinjection molding.

FIGS. 4A-C provide examples of FEA simulation data for modeling of alaser-metal wire deposition melt pool. FIG. 4A: isometric view ofcolor-encoded three dimensional FEA simulation data for the liquidfraction of material in the melt pool being deposited by a laser-metalwire deposition process. The metal is in a completely liquid state atthe position where the wire tip merges with the melt pool, andtransitions to increasingly lower liquid fractions as it solidifiesdownstream from the position of the wire. FIG. 4B: cross-sectional viewof the FEA simulation data for the liquid fraction of material in themelt pool. FIG. 4C: cross-sectional view of color-encoded threedimensional FEA simulation data for the static temperature of thematerial in the melt pool. The temperature is at a maximum value(approximately 2,900° K in this example) at the point where the laserbeam impinges on the wire tip, and is asymmetrically distributed alongthe motion path of the deposition apparatus with higher temperaturesexhibited by the material immediately downstream from the wire tip.

Process Control Parameters:

In some embodiments of the disclosed adaptive process control methods,one or more free form deposition process control parameters (or joiningprocess control parameters) may be set and/or adjusted in real-timethrough the use of a machine learning algorithm that processes real-timedeposition or welding process monitoring data, e.g., data from a machinevision system or laser interferometry measurement system, and uses thatinformation to adjust the one or more process control parameters toimprove the efficiency of the process and/or the quality of the partbeing fabricated.

In general, the types of process control parameters that may be setand/or adjusted by the adaptive process control system will varydepending on the specific type of free form deposition, additivemanufacturing, or welding process being used. Examples of processcontrol parameters that may be set and/or adjusted include, but are notlimited to, the rate of material deposition, the rate of displacementfor a deposition apparatus, the rate of acceleration for a depositionapparatus, the direction of displacement for a deposition apparatus, thelocation of a deposition apparatus as a function of time (i.e., a toolpath), the angle of a deposition apparatus with respect to a depositiondirection, the angle of overhang in an intended geometry, the intensityof heat flux into a material during deposition, the size and shape of aheat flux surface, the flow rate and angle of a shielding gas flow, thetemperature of a baseplate on which material is deposited, the ambienttemperature during a deposition process, the temperature of a depositionmaterial prior to deposition, a current or voltage setting in aresistive heating apparatus, a voltage frequency or amplitude in aninductive heating apparatus, the choice of deposition material, theratio by volume or the ratio by weight of deposition materials if morethan one deposition material is used, or any combination thereof.

As indicated above, examples of process control parameters for alaser-metal wire deposition process that may be set and/or adjusted bythe adaptive process control systems of the present disclosure include,but are not limited to, laser power, laser power distribution (or beamprofile), laser/wire or laser/substrate angle, laser beam size andshape, laser beam focal length, laser wavelength, wire feed rate, wirediameter, wire/substrate angle, wire tip position relative to the meltpool, wire stick-out, shield gas settings, feed direction, and traversespeed.

In some embodiments of the disclosed adaptive process control methodsand system, one or more process control parameters may be set and/oradjusted by the machine learning algorithm used to run the controlprocess. In some embodiments, the number of different process controlparameters to be set and/or adjusted may be at least 1, at least 2, atleast 3, at least 4, at least 5, at least 10, at least 15, or at least20. Those of skill in the art will recognize that the number ofdifferent process control parameters to be set and/or adjusted by thedisclosed process control methods and systems may have any value withinthis range, e.g., 12 process control parameters.

Process Monitoring Tools:

In some embodiments of the disclosed adaptive process control methodsand systems, one or more process monitoring tools may be used to providereal-time data on process parameters or properties of the object beingfabricated, both of which will be referred to herein as “processcharacterization data”. In some embodiments of the disclosed methods andsystem, process characterization data from past fabrication runs is usedas part of a training data set used to “teach” the machine learningalgorithm used to run the process control. In some embodiments,real-time (or “in-process”) process characterization data is fed to themachine learning algorithm so that it may adaptively adjust one or moreprocess control parameters in real-time.

Any of a variety of process monitoring tools known to those of skill inthe art may be used including, but not limited to, temperature sensors,position sensors, motion sensors, touch/proximity sensors,accelerometers, profilometers, goniometers, image sensors and machinevision systems, electrical conductivity sensors, thermal conductivitysensors, strain gauges, durometers, X-ray diffraction or imagingdevices, CT scanning devices, ultrasonic imaging devices, Eddy currentsensor arrays, thermographs, deposition apparatus status indicators, orany combination thereof. In some embodiments, the processcharacterization sensors may comprise one or more sensors that detectelectromagnetic radiation that is reflected, scattered, absorbed,transmitted, or emitted by the object. In some embodiments, the processcharacterization sensors may comprise one or more sensors that providedata on acoustic energy or mechanical energy that is reflected,scattered, absorbed, transmitted, or emitted by the object.

Any of a variety of process parameters may be monitored (i.e., togenerate process characterization data) using appropriate sensors,measurement tools, and/or machine vision systems including, but notlimited to, measurement of a bulk or peak temperature of a depositedmaterial, a cooling rate of a deposited material, a chemical compositionof a deposited material, a segregation state of constituents in adeposited material, a geometrical property of a deposited material(e.g., a local curvature of a printed part), a rate of materialdeposition, a rate of displacement for a deposition apparatus, alocation (tool path) of a deposition apparatus, an angle of a depositionapparatus with respect to a deposition direction, a deposition apparatusstatus indicator, an angle of overhang in a deposited geometry, an angleof overhang in an intended geometry, an intensity of heat flux into amaterial during deposition, an intensity of heat flux out of a materialduring deposition, an electromagnetic emission from a depositionmaterial, an acoustic emission from a deposition material, an electricalconductivity of a deposition material, a thermal conductivity of adeposition material, a defect in the geometry of an object beingfabricated, or any combination thereof.

The disclosed methods and systems for adaptive process control maycomprise the use of any number and any combination of sensors or processmonitoring tools. For example, in some embodiments, an adaptivedeposition process control system of the present disclosure may compriseat least 1, at least 2, at least 3, at least 4, at least 5, at least 6,at least 7, at least 8, at least 9, or at least 10 sensors or processmonitoring tools. In some embodiments, the one or more sensors orprocess monitoring tools may provide data to the process controlalgorithm at an update rate of at least 0.1 Hz, 1 Hz, 5 Hz, 10 Hz, 20Hz, 30 Hz, 40 Hz, 50 Hz, 60 Hz, 70 Hz, 80 Hz, 90 Hz, 100 HZ, 250 Hz, 500Hz, 750 Hz, 1,000 Hz, 2,500 Hz, 5,000 Hz, 10,000 Hz, or higher. Those ofskill in the art will recognize that the one or more sensors or processmonitoring tools may provide data at an update rate having any valuewithin this range, e.g., about 225 Hz.

Laser interferometry: One specific example of a free form deposition orjoining process monitoring tool that may be used with, for example, alaser-metal wire deposition system is a laser interferometer foraccurate, in-process measurement of part dimensions, refractive indexchanges, and/or surface irregularities. Laser light from a single sourceis split into two beams that follow separate optical paths until theyare re-combined following the transmission or reflection of one of thebeams by a sample, e.g., the part being fabricated, to produceinterference. The resulting interference fringes provide preciseinformation about the difference in optical path length for the twobeams, and hence provide precise measurements of part dimensions,displacements, surface irregularities, etc. Interferometers are capableof measuring dimensions or displacements with nanometer precision.

FIG. 5 illustrates one non-limiting example of a laser-metal wiredeposition system that comprises a robotic controller, a laser powerunit, a wire feed and shield gas module, a wire pre-heater, andenvironmental controller, a telemetry database (for transmitting andrecording process control instructions sent to and process monitoringdata read from the deposition system), and a programmable logiccontroller (which coordinates the overall operation of the systemcomponents), as well as a laser interferometer. The laser interferometerprovides real-time feedback on melt pool properties. In someembodiments, the deposition system may further comprise a processorprogrammer to utilize a machine learning algorithm, e.g., an artificialneural network, for real-time, adaptive control of the metal depositionprocess. In some embodiments, the deposition system may also includemachine vision systems or other inspection tools monitor processparameters and/or to provide for automated classification of objectdefects (post-build or in-process), and may incorporate such processmonitoring or defect classification for use by the machine algorithm inpredicting next action(s) by the deposition process.

FIG. 2 provides a schematic illustration of an example set-up for amaterial deposition process, e.g., a laser-metal wire depositionprocess, according to some embodiments of the present disclosure. Thelaser beam impinges on the metal wire to create a melt pool at the pointof intersection and deposit material on a substrate. The melt poolmaterial subsequently hardens to form a new layer as the laser and wirefeed (i.e. the print head) are moved relative to the substrate. The wireis shielded from air-borne contaminants with the use of a sheath ofshield gas. As indicated by the example of FEA simulation date presentedin FIG. 4C, heat propagates from the position of the melt pool throughthe underlying substrate (or previously deposited layers) in anasymmetric fashion due to the translational motion of the print headrelative to the substrate. The newly deposited layer forms ametallurgical bond with the substrate (or previously deposited layers)in a region referred to as the fusion zone. The propagation of heatthrough the newly deposited layer to the substrate (or previouslydeposited layers) may in some instances affect material propertieswithin a region referred to as the heat affected zone. Thesolidification process may also cause metallurgical defects such aspores and cracks to form in the deposited layer. The quantity and typeof defects that arise are dependent on the amount of heat input, thetime spent at elevated temperatures, the geometry of the printed part,and the presence of contaminants near the melt pool.

FIGS. 6A-B illustrate the use of laser interferometry to monitor meltpool and deposition layer properties in a laser-metal wire depositionprocess. FIG. 6A shows a micrograph of the deposition process at thelocation where the laser beam impinges on the metal wire. The verticallines indicate the position of the interferometer probe beam as it isused to monitor the height profile of the wire feed and previouslydeposited layer and resulting melt pool. FIG. 6B provides examples ofcross-sectional profiles (i.e., height profiles across the width of thedeposition) of the wire feed, previously deposited layer, and melt poolas measured using laser interferometry at the position of the wire feed(solid line; the peak indicates the wire, while the shoulders indicatethe height of the previously deposited layer) and the melt pool (dashedline). The x-axis (width) dimension is plotted in arbitrary units. They-axis (height) dimension is plotted in units of millimeters relative toa fixed reference point below the deposition layer. In some embodimentsof the disclosed adaptive process control methods, such real-timeprocess monitoring data may be used by a processor running a machinelearning algorithm to make adjustment(s) to one or more process controlparameters in order to improve, for example, the dimensional accuracy ofthe layer, layer surface finish and/or adhesion properties, and/or theoverall efficiency of the deposition process.

In some embodiments, laser interferometry may be used to monitor thedimensions and/or properties of the melt pool, the deposited layerdownstream from the melt pool, or other features of the part beingfabricated at one or more positions on the part. In some embodiment,laser interferometry may be used to monitor the dimensions and/orproperties of the part being fabricated at at least 1, at least 2, atleast 3, at least 4, at least 5, at least 6, at least 7, at least 8, atleast 9, or at least 10 different positions on the part. In someembodiment, the laser interferometry data for dimensions and/or otherproperties of the part may be updated at a rate of at least 0.1 Hz, 1Hz, 5 Hz, 10 Hz, 20 Hz, 30 Hz, 40 Hz, 50 Hz, 60 Hz, 70 Hz, 80 Hz, 90 Hz,100 HZ, 250 Hz, 500 Hz, 750 Hz, 1,000 Hz, 2,500 Hz, 5,000 Hz, 10,000 Hz,25,000 Hz, 50,000 Hz, 100,000 Hz, 150,000 Hz, 200,000 Hz, 250,000 Hz, orhigher. Those of skill in the art will recognize that the rate at whichthe interferometry data may be updated may have any value within thisrange, e.g., about 800 Hz.

Machine vision systems: Another specific example of a free formdeposition or joining process monitoring tool that may be used with, forexample, a laser-metal wire deposition system is machine vision. Machinevision systems provide imaging-based automatic inspection and analysisfor a variety of industrial inspection, process control, and robotguidance applications, and may comprise any of a variety of imagesensors or cameras, light sources or illumination systems, andadditional imaging optical components, as well as processors and imageprocessing software.

FIGS. 7A-C illustrate in-process feature extraction from images of alaser-metal wire deposition process obtained using a machine visionsystem. FIG. 7A shows a raw image (e.g., one image frame grabbed from avideo rate data stream) of the melt pool adjacent to the tip of thewire. FIG. 7B shows the processed image after de-noising, filtering, andedge detection algorithms have been applied. FIG. 7C shows the processedimage after application of a feature extraction algorithm used toidentify, for example, the angel of the wire relative to the build plateand the height (thickness) of the new layer. Machine vision systems andthe associated image processing capability allow one to monitor detailsof the deposition process in real-time.

In some embodiments, one or more machine vision systems may be used withthe disclosed adaptive process control methods and systems to acquireand process single images. In some embodiments, one or more machinevision systems may be used with the disclosed adaptive process controlmethods and systems to acquire and process a series of one or moreimages at defined time intervals. In many embodiments, one or moremachine vision systems may be used with the disclosed adaptive processcontrol methods and systems to acquire and process video rate imagedata. In general, image data supplied by the one or more machine visionsystems may be acquired and/or processed at a rate of at least 0.1 Hz, 1Hz, 5 Hz, 10 Hz, 20 Hz, 30 Hz, 40 Hz, 50 Hz, 60 Hz, 70 Hz, 80 Hz, 90 Hz,100 HZ, 250 Hz, 500 Hz, 750 Hz, 1,000 Hz, 2,500 Hz, 5,000 Hz, or higher.Those of skill in the art will recognize that the rate at which imagedata may be acquired and/or processed may have any value within thisrange, e.g., 95 Hz.

In some embodiments, one or more machine vision systems used with thedisclosed adaptive process control methods and systems may be configuredto acquire images at specific wavelengths (or within specific wavelengthranges) or in different imaging modes. For example, in some embodiments,one or more machine vision system may be configured to acquire images inthe x-ray region, ultraviolet region, visible region, near infraredregion, infrared region, terahertz region, microwave region, orradiofrequency region of the electromagnetic spectrum, or anycombination thereof. In some embodiments, one or more machine visionsystems may be configured to acquire fluorescence images (e.g., wherethe wavelength range for the excitation light is different than that forthe collected fluorescence emission light). In some embodiments, one ormore machine vision systems may be configured to acquire coherent Ramanscattering (CRS) images (e.g., stimulated Raman scattering (SRS) oranti-Stokes Raman scattering (CARS) images) to provide label-freechemical imaging of the deposition layer or part being fabricated.

Post-Build Inspection Tools and Automated Defect Classification:

Disclosed herein are automated object defect classification methods andsystems used to identify and characterize defects in fabricated parts.The approach is based on the use of a machine learning algorithm fordetection and classification of defects, where the machine learningalgorithm is trained using a training dataset that comprises post-buildinspection data provided by a skilled operator and/or inspection dataprovided by any of a variety of automated inspection tools known tothose of skill in the art. The disclosed automated object defectclassification methods and systems may be applied to any of a variety offree form deposition or joining processes known to those of skill in theart. In some embodiments, the disclosed automated object defectclassification methods and systems may be used strictly for post-buildinspection of new parts. In some embodiments, they may be usedin-process to provide real-time process characterization data to amachine learning algorithm used to run the process control, so that oneor more process control parameters may be adjusted in real-time. In someembodiments, the disclosed automated object defect classificationmethods and systems may be used both in-process to provide real-timeprocess characterization data and for post-build inspection. In someembodiments, in-process automated defect classification data may be usedby the machine learning algorithm to determine a set or sequence ofprocess control parameter adjustments that will implement a correctiveaction, e.g., to adjust a layer dimension or thickness, so as to correcta defect when first detected. In some embodiments, in-process automateddefect classification may be used by the machine learning algorithm tosend a warning or error signal to an operator, or optionally, toautomatically abort the deposition process, e.g., an additivemanufacturing process. In some embodiments, once trained, the automateddefect classification system requires no further user input (e.g., nofurther input from a skilled operator or inspector) to detect andclassify defects either in-process and/or post-build.

The automated object defect classification methods will generallycomprise: a) providing a training data set, wherein the training dataset comprises fabrication process simulation data, fabrication processcharacterization data, and/or post-build inspection data, or anycombination thereof, for a plurality of design geometries that are thesame as or different from that of the object; b) providing one or moresensors, wherein the one or more sensors provide real-time data for oneor more object properties; c) providing a processor programmed toprovide a classification of detected object defects using a machinelearning algorithm that has been trained using the training data set ofstep (a), wherein the real-time data from the one or more sensors isprovided as input to the machine learning algorithm and allows theclassification of detected object defects to be adjusted in real-time.

Training data sets: As noted above, the training data set may comprisefabrication process simulation data, fabrication processcharacterization data, post-build inspection data (including inspectiondata provided by a skilled operator and/or inspection data provided byany of a variety of automated inspection tools), or any combinationthereof, for past fabrication runs of a plurality of design geometriesthat are the same as or different from that of the object currentlybeing fabricated. One or more training data sets may be used to trainthe machine learning algorithm used for object defect detection andclassification. In some cases, the type of data included in the trainingdata set may vary depending on the specific type of machine learningalgorithm employed, as will be discussed in more detail below. Forexample, in the case that an expert system (or expert learning system)the training data set may comprise primarily defect classification dataprovided by a skilled operator or technician in visually identifying andclassifying object defects for the same type of part or for a variety ofdifferent parts that share some common set of features. In someinstances, the training data set may be updated in real-time with objectdefect and object classification date as it is performed on a givensystem. In some instances, the training data may be updated with objectdefect data and object classification data drawn from a plurality ofautomated defect classification systems.

In some embodiments, the training data set may comprise processsimulation data, process characterization data, in-process inspectiondata, post-build inspection data, or any combination thereof. In someembodiments, the training data set may comprise a single type of dataselected from the group consisting of process simulation data, processcharacterization data, in-process inspection data, and post-buildinspection data. In some embodiments, the training data set may comprisea combination of any two or any three types of data selected from thegroup consisting of process simulation data, process characterizationdata, in-process inspection data, and post-build inspection data. Insome embodiments, the training data set may comprise all of these typesof data, i.e., process simulation data, process characterization data,in-process inspection data, and post-build inspection data.

Object property measurement: Any of a variety of sensors or otherinspection tools may be used, including some of those listed above forprocess monitoring in general. In some embodiments, the one or moresensors (e.g., image sensors or machine vision systems) provide data onelectromagnetic radiation that is reflected, scattered, absorbed,transmitted, or emitted by the object. In some embodiments, theelectromagnetic radiation is x-ray, ultraviolet, visible, near-infrared,or infrared light. In some embodiments, the one or more sensors providedata on acoustic energy that is reflected, scattered, absorbed,transmitted, or emitted by the object. In some embodiments, the one ormore sensors provide data on an electrical conductivity or a thermalconductivity of the object. In some embodiments, the one or more sensorsmay provide data to the processor programmed to provide a classificationof detected object defects using a machine learning algorithm at anupdate rate of at least 0.1 Hz, 1 Hz, 5 Hz, 10 Hz, 20 Hz, 30 Hz, 40 Hz,50 Hz, 60 Hz, 70 Hz, 80 Hz, 90 Hz, 100 HZ, 250 Hz, 500 Hz, 750 Hz, 1,000Hz, 2,500 Hz, 5,000 Hz, 10,000 Hz, or higher. Those of skill in the artwill recognize that the one or more sensors or process monitoring toolsmay provide data at an update rate having any value within this range,e.g., about 400 Hz.

In a preferred embodiment the automated object defect classificationmethods and systems of the present disclosure may be implemented usingimage sensors and/or machine vision systems. Automated image processingof the captured images may then be used to monitor any of a variety ofobject properties, e.g., dimensions (overall dimensions, or dimensionsof specific features), feature angles, feature areas, surface finish(e.g., degree of light reflectivity, number of pits and/or scratches perunit area), and the like. In some embodiments, object properties such aslocal, excessively high temperatures that may be correlated with defectsor defect generation in printed or welded parts may be monitored usinginfrared or visible wavelength cameras.

Noise removal from sensor data: In some embodiments, the automateddefect classification methods may further comprise removing noise fromthe object property data provided by the one or more sensors prior toproviding it to the machine learning algorithm. Examples of dataprocessing algorithms suitable for use in removing noise from the objectproperty data provided by the one or more sensors include, but are notlimited to, signal averaging algorithms, smoothing filter algorithms,Kalman filter algorithms, nonlinear filter algorithms, total variationminimization algorithms, or any combination thereof.

Subtraction of reference data sets: In some embodiments of the disclosedautomated defect classification methods, subtraction of a reference dataset from the sensor data may be used to increase contrast between normaland defective features of the object, thereby facilitating defectdetection and classification. For example, a reference data set maycomprise sensor data recorded by one or more sensors for an ideal,defect-free example of the object to be fabricated. In the case that animage sensor or machine vision system is used for defect detection, thereference data set may comprise an image (or set of images, e.g.,representing different views) of an ideal, defect-free object.

Machine learning algorithms for defect detection and classification: Anyof a variety of machine learning algorithms may be used in implementingthe disclosed automated object defect detection and classificationmethods. The machine learning algorithm employed may comprise asupervised learning algorithm, an unsupervised learning algorithm, asemi-supervised learning algorithm, a reinforcement learning algorithm,a deep learning algorithm, or any combination thereof. In preferredembodiments, the machine learning algorithm employed for defectidentification and classification may comprise a support vector machine(SVM), an artificial neural network (ANN), or a decision tree-basedexpert learning system, some of which will be described in more detailbelow. In some preferred embodiments, object defects may be detected asdifferences between an object property data set and a reference data setthat are larger than a specified threshold, and may be classified usinga one-class support vector machine (SVM) or autoencoder algorithm. Insome preferred embodiments, object defects may be detected andclassified using an unsupervised one-class support vector machine (SVM),autoencoder, clustering, or nearest neighbor (e.g., kNN) machinelearning algorithm and a training data set that comprises objectproperty data for both defective and defect-free objects.

Adaptive, Real-Time Deposition Process Control Using a Machine LearningAlgorithm:

Disclosed herein are methods and systems for providing real-timeadaptive control of deposition processes, e.g., additive manufacturingor welding processes. In general, the disclosed methods comprise a)providing an input design geometry for an object (e.g., a 3D CAD model);b) providing a training data set, wherein the training data setcomprises process simulation data, process characterization data,post-build inspection data, or any combination thereof, for a pluralityof design geometries or portions thereof that are the same as ordifferent from the input design geometry of step (a); c) providing apredicted optimal set or sequence of one or more process controlparameters for fabricating the object, wherein the predicted optimal setof one or more process control parameters are derived using a machinelearning algorithm that has been trained using the training data set ofstep (b); and d) performing the deposition process, e.g., an additivemanufacturing process, to fabricate the object, wherein real-timeprocess characterization data is provided by one or more sensors asinput to the machine learning algorithm to adjust one or more processcontrol parameters in real-time. In some embodiments, steps (b)-(d) areperformed iteratively and the process characterization data, post-buildinspection data, or any combination thereof for each iteration isincorporated into the training data set. The disclosed process controlmethods may be used for any of a variety of deposition processes,including additive manufacturing processes, known to those of skill inthe art, for example, stereolithography (SLA), digital light processing(DLP), fused deposition modeling (FDM), selective laser sintering (SLS),selective laser melting (SLM), electronic beam melting (EBM) process,laser beam welding, MIG (metal inert gas) welding, TIG (tungsten inertgas) welding, and the like. In a preferred embodiment, the disclosedprocess control methods are applied to a liquid-to-solid free formdeposition process, for example, to a laser metal-wire depositionprocess.

Training data sets: As with the automated defect classification methodsdescribed above, the training data set(s) used in teaching the processcontrol machine learning algorithm may comprise fabrication processsimulation data, fabrication process characterization data, post-buildinspection data (including inspection data provided by a skilledoperator and/or inspection data provided by any of a variety ofautomated inspection tools), or any combination thereof, for pastfabrication runs of a plurality of design geometries that are the sameas or different from that of the object currently being fabricated. Oneor more training data sets may be used to train the machine learningalgorithm used for adaptive, real-time deposition process control. Insome cases, the type of data included in the training data set may varydepending on the specific type of machine learning algorithm employed,as will be discussed in more detail below. For example, in some casesthe training data set may comprise primarily process control settingsprovided by a skilled operator or technician in successfully fabricatinga number of the same type of part or for a variety of different partsthat share some common set of features. In some instances, the trainingdata set may be updated in real-time using process simulation data,process control data, process characterization data, in-processinspection data, and/or post-build inspection data as fabrication isperformed on a given system. In some instances, the training data may beupdated using process simulation data, process control data, processcharacterization data, in-process inspection data, and/or post-buildinspection data as fabrication is performed on a plurality of depositionand/or welding systems.

In some embodiments, the training data set may comprise processsimulation data, process characterization data, in-process inspectiondata, post-build inspection data, or any combination thereof. In someembodiments, the training data set may comprise a single type of dataselected from the group consisting of process simulation data, processcharacterization data, in-process inspection data, and post-buildinspection data. In some embodiments, the training data set may comprisea combination of any two or any three types of data selected from thegroup consisting of process simulation data, process characterizationdata, in-process inspection data, and post-build inspection data. Insome embodiments, the training data set may comprise all of these typesof data, i.e., process simulation data, process characterization data,in-process inspection data, and post-build inspection data.

Process characterization data: Any of a variety of sensors, measurementtools, or inspection tools may be used for monitoring various processparameters in real-time, including those listed above. In someembodiments, for example, laser interferometers are used to monitor thedimensions of the melt pool (in the case of laser-metal wire deposition)or other part dimensions as the part is being fabricated. In someembodiments, the one or more sensors (e.g., image sensors or machinevision systems) provide data on electromagnetic radiation that isreflected, scattered, absorbed, transmitted, or emitted by the object.In some embodiments, the electromagnetic radiation is x-ray,ultraviolet, visible, near-infrared, or infrared light. In someembodiments, real-time image acquisition and processing is used tomonitor, for example, the angle of the wire feed relative to a baseplateor previously deposited layer, or the thickness of a deposited layer. Insome embodiments, the one or more sensors provide data on acousticenergy that is reflected, scattered, absorbed, transmitted, or emittedby the object. In some embodiments, the one or more sensors provide dataon an electrical conductivity or a thermal conductivity of the object.In some embodiments, the one or more sensors may provide processcharacterization data to the processor programmed to run the machinelearning algorithm may be updated at a rate of at least 0.1 Hz, 1 Hz, 5Hz, 10 Hz, 20 Hz, 30 Hz, 40 Hz, 50 Hz, 60 Hz, 70 Hz, 80 Hz, 90 Hz, 100HZ, 250 Hz, 500 Hz, 750 Hz, 1,000 Hz, 2,500 Hz, 5,000 Hz, 10,000 Hz, orhigher. Those of skill in the art will recognize that the one or moreprocess characterization sensor may provide data at an update ratehaving any value within this range, e.g., about 8,000 Hz.

In a preferred embodiment, the real-time process characterization datathat is fed to the machine learning algorithm used to run processcontrol may comprise data supplied by an automated object defectclassification system as described above, so that the deposition processcontrol parameters may be adjusted in real-time to compensate or correctfor part defects as they arise during the build process. The machinelearning algorithm used to run the automated process control may beconfigured to adjust the process control parameters in real-time asnecessary to maximize a reward function (or to minimize a lossfunction), as will be discussed in more detail below.

Machine learning algorithms for automated deposition process control:Any of a variety of machine learning algorithms may be used inimplementing the disclosed process control methods, and may be the sameor different from those used to implement the automated object defectclassification methods described above. The machine learning algorithmemployed may comprise a supervised learning algorithm, an unsupervisedlearning algorithm, a semi-supervised learning algorithm, areinforcement learning algorithm, a deep learning algorithm, or anycombination thereof. In preferred embodiments, the machine learningalgorithm employed may comprise an artificial neural network algorithm,a Gaussian process regression algorithm, a logistical model treealgorithm, a random forest algorithm, a fuzzy classifier algorithm, adecision tree algorithm, a hierarchical clustering algorithm, a k-meansalgorithm, a fuzzy clustering algorithm, a deep Boltzmann machinelearning algorithm, a deep convolutional neural network algorithm, adeep recurrent neural network, or any combination thereof, some of whichwill be described in more detail below.

Reward functions and loss functions: As noted above, in some embodimentsthe machine learning algorithm used to run the automated process controlmay be configured to adjust the process control parameters in real-timeas necessary to maximize a reward function (or to minimize a lossfunction) in order to optimize the deposition process. As used herein, areward function (or conversely, a loss function (sometimes also referredto as a cost function or error function)) refers to a function that mapsthe values of one or more additive manufacturing process variablesand/or fabrication event outcomes to a real number that represents the“reward” associated with a given fabrication event (or the “cost” in thecase of a loss function). Examples of process parameters and fabricationevent outcomes that may be used in defining a reward (or loss) functioninclude, but are not limited to, process throughput (e.g. number ofparts fabricated per unit time), process yield (e.g., the percentage ofparts produced that meet a specified set of quality criteria),production quality (e.g., mean squared deviation in part dimension(s)between the parts produced and an ideal, defect-free reference part, orthe average number of defects detected per part produced), productioncost (e.g., the cost per part produced), and the like. In some cases,the definition of the reward function (or loss function) to be maximized(or minimized) may be dependent on the choice of machine learningalgorithm used to run the process control method, and vice versa. Forexample, if the objective is to maximize a total reward/value function,a reinforcement learning algorithm may be chosen. If the objective is tominimize a mean squared error cost (or loss) function, a decision treeregression algorithm or linear regression algorithm may be chosen. Ingeneral, the machine learning algorithm used to run the process controlmethod will seek to optimize the reward function (or minimize the lossfunction) by (i) identifying the current “state” of the part underfabrication (e.g., based on the real-time stream of processcharacterization data supplied by one or more sensors), (ii) comparingthe current “state” to the design target (or reference “state”), and(iii) adjusting one or more process control parameters in order tominimize the difference between the two states (e.g., based on past“learning” provided by the training data set).

FIG. 8 illustrates an action prediction—reward loop for a reinforcementlearning algorithm according to some embodiments of the discloseddeposition or welding process control methods. In the case of adeposition process, for example, at any point in time during orfollowing completion of layer deposition (action a_(j)), the part beingfabricated is monitored using any of a variety of sensors, measuringtools, inspection tools, and/or machine vision systems as describedabove to determine the current build “state” of the part (state s_(j)).In a preferred embodiment, the part is monitored in real-time using anautomated object defect classification system as disclosed herein. Oncethe current build state of the part has been determined, a reinforcementlearning algorithm uses the current state information, s_(j), and themodel developed using past training data to predict a proposed action,a_(j+1), (e.g., a set or sequence of process control parameteradjustments) that will maximize a reward function. If the current buildstate, s_(j), is relatively poor (i.e., associated with a low value ofthe reward function), it may not be desirable to simply take the set ofactions that produces the highest reward in the next build state,s_(j+1), because that may not produce the maximum reward in the longrun. In some cases, maximizing the reward for the immediate next buildstate, s_(j+1), may force a decision between very low reward states fornext few build states, e.g., s_(j+2), s_(j+3), s_(j+4), thereafter. Byusing the learned process model to look a bit further into the future,one can optimize the process control parameter adjustments for the nextN build states as opposed to just the immediate next state. Each set of“next N states” starting from state s_(i) has a corresponding reward(i.e., the reward space for the next N actions) that can be predictedusing the previously trained model that predicts the correlation betweenactions and their resulting state. Thus the learned model may be used todetermine a sequence of actions that optimizes the sum (or weighted sum)of reward values for the next N states. The loop is repeated until thepart is complete, and provides adaptive control of the depositionprocess to provide for rapid optimization and adjustment of the processcontrol parameters used in response to changes in process orenvironmental parameters, as well as improved process yield, processthroughput, and quality of the parts.

FIG. 9 illustrates reward function construction where the training dataused to generate the reward function-based state prediction model isacquired by monitoring the actions that a human operator chooses duringa manually-controlled deposition process. In some embodiments, themachine learning algorithm may be wholly or partially self-trained. Forexample, in some embodiments, as part of the training of the machinelearning algorithm, the machine learning algorithm may randomly choosevalues within a specified range for each of a set of one or more processcontrol parameters, and incorporate the resulting process simulationdata, process characterization data, in-process inspection data,post-build inspection data, or any combination thereof, into thetraining data set to improve a learned model that maps process controlparameter values to process outcomes.

In general, the methods and systems for adaptive, real-time control ofdeposition processes that are disclosed herein do not rely on staticdata look-up operations (e.g., looking up process control parameters orprocess characterization data from previous runs). Rather, a machinelearning algorithm is used to explore a range of input values for one ormore process control parameters during process simulation and/or actualpart fabrication, and generates a learned model that maps input processcontrol parameters to process outcomes under a variety of differentprocess and environmental conditions.

Process control parameter update rates: In some embodiments, the one ormore sensors may provide data to the processor programmed to run amachine learning algorithm so that one or more process controlparameters may be adjusted at an update rate of at least 0.1 Hz, 1 Hz, 5Hz, 10 Hz, 20 Hz, 30 Hz, 40 Hz, 50 Hz, 60 Hz, 70 Hz, 80 Hz, 90 Hz, 100HZ, 250 Hz, 500 Hz, 750 Hz, 1,000 Hz, 2,500 Hz, 5,000 Hz, 10,000 Hz, orhigher. Those of skill in the art will recognize that the one or moreprocess control parameters may be adjusted or updated at a rate havingany value within this range, e.g., about 8,000 Hz.

Machine Learning Algorithms for Adaptive Process Control:

As noted above, the machine learning algorithm(s) employed in thedisclosed automated defect classification and additive manufacturingprocess control methods may comprise a supervised learning algorithm, anunsupervised learning algorithm, a semi-supervised learning algorithm, areinforcement learning algorithm, a deep learning algorithm, or anycombination thereof.

Supervised learning algorithms: In the context of the presentdisclosure, supervised learning algorithms are algorithms that rely onthe use of a set of labeled training data to infer the relationshipbetween a set of one or more defects identified for a given object and aclassification of the object according to a specified set of qualitycriteria, or to infer the relationship between a set of input additivemanufacturing process control parameters and a set of desiredfabrication outcomes. The training data comprises a set of pairedtraining examples, e.g., where each example comprises a set of defectsdetected for a given object and the resultant classification of thegiven object, or where each example comprises a set of process controlparameters that were used in a fabrication process that is paired withthe known outcome of the fabrication process.

Unsupervised learning algorithms: In the context of the presentdisclosure, unsupervised learning algorithms are algorithms used to drawinferences from training datasets consisting of object defect datasetsthat are not paired with labeled object classification data, or inputadditive manufacturing process control parameter data that are notpaired with labeled fabrication outcomes. The most commonly usedunsupervised learning algorithm is cluster analysis, which is often usedfor exploratory data analysis to find hidden patterns or groupings inprocess data.

Semi-supervised learning algorithms: In the context of the presentdisclosure, semi-supervised learning algorithms are algorithms that makeuse of both labeled and unlabeled object classification or additivemanufacturing process data for training (typically using a relativelysmall amount of labeled data with a large amount of unlabeled data).

Reinforcement learning algorithms: In the context of the presentdisclosure, reinforcement learning algorithms are algorithms which areused, for example, to determine a set of additive manufacturing processsteps (or actions) that should be taken so as to maximize a specifiedfabrication process reward function. In machine learning environments,reinforcement learning algorithms are often formulated as Markovdecision processes. Reinforcement learning algorithms differ fromsupervised learning algorithms in that correct training datainput/output pairs are never presented, nor are sub-optimal actionsexplicitly corrected. These algorithms tend to be implemented with afocus on real-time performance through finding a balance betweenexploration of possible outcomes based on updated input data andexploitation of past training.

Deep learning algorithms: In the context of the present disclosure, deeplearning algorithms are algorithms inspired by the structure andfunction of the human brain called artificial neural networks (ANNs),and specifically large neural networks comprising many layers, that areused to map object defect data to object classification decisions, or tomap input additive manufacturing process control parameters to desiredfabrication outcomes. Artificial neural networks will be discussed inmore detail below.

Decision tree-based expert systems: In the context of the presentdisclosure, expert systems are one example of supervised learningalgorithms that are designed to solve object defect classificationproblems or additive manufacturing process control problems by applyinga series of if—then rules. Expert systems typically comprise twosubsystems: an inference engine and a knowledge base. The knowledge basecomprises a set of facts (e.g., a training data set comprising objectdefect data for a series of fabricated parts, and the associated objectclassification data provided by a skilled operator, technician, orinspector) and derived rules (e.g., derived object classificationrules). The inference engine then applies the rules to data for acurrent object classification problem or process control problem todetermine a classification of the object or a next set of processcontrol adjustments.

Support vector machines (SYMs): In the context of the presentdisclosure, support vector machines are supervised learning algorithmsused for classification and regression analysis of object defectclassification date or additive manufacturing process control. Given aset of training data examples (e.g., object defect data), each marked asbelonging to one or the other of two categories (e.g., good or bad, passor fail), an SVM training algorithm builds a model that assigns newexamples (e.g., defect data for a newly fabricated object) to onecategory or the other.

Autoencoders: In the context of the present disclosure, an autoencoder(also sometimes referred to as an autoassociator or Diabolo network) isan artificial neural network used for unsupervised, efficient mapping ofinput data, e.g., object defect data, to an output value, e.g., anobject classification. Autoencoders are often used for the purpose ofdimensionality reduction, i.e., the process of reducing the number ofrandom variables under consideration by deducing a set of principalcomponent variables. Dimensionality reduction may be performed, forexample, for the purpose of feature selection (i.e., a subset of theoriginal variables) or feature extraction (i.e., transformation of datain a high-dimensional space to a space of fewer dimensions).

Artificial neural networks (ANNs): In some cases, the machine learningalgorithm used for the disclosed automated object defect classificationor adaptive process control methods may comprise an artificial neuralnetwork (ANN), e.g., a deep machine learning algorithm. The automatedobject classification methods of the present disclosure may, forexample, employ an artificial neural network to map object defect datato object classification data. The additive manufacturing processcontrol systems of the present disclosure may, for example, employ anartificial neural network (ANN) to determine an optimal set or sequenceof process control parameter settings for adaptive control of anadditive manufacturing process in real-time based on a stream of processmonitoring data and/or object defect classification data provided by oneor more sensors. The artificial neural network may comprise any type ofneural network model, such as a feedforward neural network, radial basisfunction network, recurrent neural network, or convolutional neuralnetwork, and the like. In some embodiments, the automated object defectclassification and additive manufacturing process control methods andsystems of the present disclosure may employ a pre-trained ANNarchitecture. In some embodiment, the automated object defectclassification and additive manufacturing process control methods andsystems of the present disclosure may employ an ANN architecture whereinthe training data set is continuously updated with real-time objectclassification data or real-time deposition process control andmonitoring data from a single local system, from a plurality of localsystems, or from a plurality of geographically distributed systems.

As used throughout this disclosure, the term “real-time” refers to therate at which sensor data (e.g. process control data, process monitoringdata, and/or object defect identification and classification data) isacquired, processed, and/or used by a machine learning algorithm, e.g.,an artificial neural network or deep machine learning algorithm, toupdate a prediction of object classification or a prediction of optimalprocess control parameters in response to changes in one or more of theinput sensor data streams. In general the update rate for the objectclassification or process control parameters provided by the disclosedobject defect classification and additive manufacturing process controlmethods and systems may range from about 0.1 Hz to about 10,000 Hz. Insome embodiments, the update rate may be at least 0.1 Hz, at least 1 HZ,at least 10 Hz, at least 50 Hz, at least 100 Hz, at least 250 Hz, atleast 500 Hz, at least 750 Hz, at least 1,000 Hz, at least 2,000 Hz, atleast 3,000 Hz, at least 4,000 Hz, at least 5,000 Hz, or at least 10,000Hz. In some embodiments, the update rate may be at most 10,000 Hz, atmost 5,000 Hz, at most 4,000 Hz, at most 3,000 Hz, at most 2,000 Hz, atmost 1,000 Hz, at most 750 Hz, at most 500 Hz, at most 250 Hz, at most100 Hz, at most 50 Hz, at most 10 Hz, at most 1 Hz, or at most 0.1 Hz.Those of skill in the art will recognize that the update rate may haveany value within this range, for example, about 8,000 Hz.

Artificial neural networks generally comprise an interconnected group ofnodes organized into multiple layers of nodes (see FIG. 10). Forexample, the ANN architecture may comprise at least an input layer, oneor more hidden layers, and an output layer. The ANN may comprise anytotal number of layers, and any number of hidden layers, where thehidden layers function as trainable feature extractors that allowmapping of a set of input data to a preferred output value or set ofoutput values. Each layer of the neural network comprises a number ofnodes (or neurons). A node receives input that comes either directlyfrom the input data (e.g., sensor data, image data, object defect data,etc., in the case of the presently disclosed methods) or the output ofnodes in previous layers, and performs a specific operation, e.g., asummation operation. In some cases, a connection from an input to a nodeis associated with a weight (or weighting factor). In some cases, thenode may sum up the products of all pairs of inputs, x₁, and theirassociated weights, w_(i) (FIG. 11). In some cases, the weighted sum isoffset with a bias, b, as illustrated in FIG. 11. In some cases, theoutput of a neuron may be gated using a threshold or activationfunction, f which may be a linear or non-linear function. The activationfunction may be, for example, a rectified linear unit (ReLU) activationfunction or other function such as a saturating hyperbolic tangent,identity, binary step, logistic, arcTan, softsign, parameteric rectifiedlinear unit, exponential linear unit, softPlus, bent identity,softExponential, Sinusoid, Sinc, Gaussian, or sigmoid function, or anycombination thereof.

The weighting factors, bias values, and threshold values, or othercomputational parameters of the neural network, can be “taught” or“learned” in a training phase using one or more sets of training data.For example, the parameters may be trained using the input data from atraining data set and a gradient descent or backward propagation methodso that the output value(s) (e.g., a set of predicted adjustments toprocess control parameter settings) that the ANN computes are consistentwith the examples included in the training data set. The parameters maybe obtained from a back propagation neural network training process thatmay or may not be performed using the same hardware as that used forautomated object defect classification or adaptive, real-time depositionprocess control.

Other specific types of deep machine learning algorithms, e.g.,convolutional neural networks (CNNs) (e.g., for the processing of imagedata from machine vision systems) may also be used by the disclosedmethods and systems. CNN are commonly composed of layers of differenttypes: convolution, pooling, upscaling, and fully-connected node layers.In some cases, an activation function such as rectified linear unit maybe used in some of the layers. In a CNN architecture, there can be oneor more layers for each type of operation performed. A CNN architecturemay comprise any number of layers in total, and any number of layers forthe different types of operations performed. The simplest convolutionalneural network architecture starts with an input layer followed by asequence of convolutional layers and pooling layers, and ends withfully-connected layers. Each convolution layer may comprise a pluralityof parameters used for performing the convolution operations. Eachconvolution layer may also comprise one or more filters, which in turnmay comprise one or more weighting factors or other adjustableparameters. In some instances, the parameters may include biases (i.e.,parameters that permit the activation function to be shifted). In somecases, the convolutional layers are followed by a layer of ReLUactivation function. Other activation functions can also be used, forexample the saturating hyperbolic tangent, identity, binary step,logistic, arcTan, softsign, parameteric rectified linear unit,exponential linear unit, softPlus, bent identity, softExponential,Sinusoid, Sinc, Gaussian, the sigmoid function and various others. Theconvolutional, pooling and ReLU layers may function as learnablefeatures extractors, while the fully connected layers may function as amachine learning classifier.

As with other artificial neural networks, the convolutional layers andfully-connected layers of CNN architectures typically include variouscomputational parameters, e.g., weights, bias values, and thresholdvalues, that are trained in a training phase as described above.

In general, the number of nodes used in the input layer of the ANN(which enable input of data from multiple sensor data streams and/or,for example, sub-sampling of an image frame) may range from about 10 toabout 10,000 nodes. In some instances, the number of nodes used in theinput layer may be at least 10, at least 50, at least 100, at least 200,at least 300, at least 400, at least 500, at least 600, at least 700, atleast 800, at least 900, at least 1000, at least 2000, at least 3000, atleast 4000, at least 5000, at least 6000, at least 7000, at least 8000,at least 9000, or at least 10,000. In some instances, the number of nodeused in the input layer may be at most 10,000, at most 9000, at most8000, at most 7000, at most 6000, at most 5000, at most 4000, at most3000, at most 2000, at most 1000, at most 900, at most 800, at most 700,at most 600, at most 500, at most 400, at most 300, at most 200, at most100, at most 50, or at most 10. Those of skill in the art will recognizethat the number of nodes used in the input layer may have any valuewithin this range, for example, about 512 nodes.

In some instance, the total number of layers used in the ANN (includinginput and output layers) may range from about 3 to about 20. In someinstance the total number of layer may be at least 3, at least 4, atleast 5, at least 10, at least 15, or at least 20. In some instances,the total number of layers may be at most 20, at most 15, at most 10, atmost 5, at most 4, or at most 3. Those of skill in the art willrecognize that the total number of layers used in the ANN may have anyvalue within this range, for example, 8 layers.

In some instances, the total number of learnable or trainableparameters, e.g., weighting factors, biases, or threshold values, usedin the ANN may range from about 1 to about 10,000. In some instances,the total number of learnable parameters may be at least 1, at least 10,at least 100, at least 500, at least 1,000, at least 2,000, at least3,000, at least 4,000, at least 5,000, at least 6,000, at least 7,000,at least 8,000, at least 9,000, or at least 10,000. Alternatively, thetotal number of learnable parameters may be any number less than 100,any number between 100 and 10,000, or a number greater than 10,000. Insome instances, the total number of learnable parameters may be at most10,000, at most 9,000, at most 8,000, at most 7,000, at most 6,000, atmost 5,000, at most 4,000, at most 3,000, at most 2,000, at most 1,000,at most 500, at most 100 at most 10, or at most 1. Those of skill in theart will recognize that the total number of learnable parameters usedmay have any value within this range, for example, about 2,200parameters.

Integrated and Distributed Additive Manufacturing Systems:

In some embodiments, the adaptive, real-time process control methods ofthe present disclosure may be used for integrated additive manufacturingand/or welding systems (i.e., free form deposition or joining systems)that reside at a single physical/geographical location. FIG. 12 providesa schematic illustration of an integrated additive manufacturing systemcomprising a deposition apparatus, one or more machine vision systemsand/or other process monitoring tools, process simulation tools,post-build inspection tools, and one or more processors for running amachine learning algorithm that utilizes data from the processsimulation tools, machine vision and/or process monitoring tools(including in-process inspection and/or defect classification tools),post-build inspection tools, or any combination thereof, to providereal-time adaptive control of the deposition process, where thecomponents of the system are located in the same physical/geographicallocation. In these embodiments, the processor may communicate with theindividual system components through direct, hard-wired connectionsand/or via short-range communication links such as blue tooth or wificonnections. In some embodiments, two or more of the system componentsmay be housed within an enclosure or housing (dashed line) that enablestighter control of fabrication environmental parameters such astemperature, pressure, atmospheric composition, etc.

FIG. 13 provides a schematic illustration of a distributed free formdeposition system, e.g., an additive manufacturing system, comprisingone or more deposition apparatus, process simulation tools, machinevision systems and/or other process monitoring tools, in-processinspection tools, post-build inspection tools, and one or moreprocessors for running a machine learning algorithm that utilizes datafrom the machine vision and/or process monitoring tools, the processsimulation tools, the post-build inspection tools, or any combinationthereof, to provide real-time adaptive control of the depositionprocess, where the different components or modules of the system may bephysically located in different workspaces and/or worksites (i.e.different physical/geographical locations), and may be linked via alocal area network (LAN), an intranet, an extranet, or the internet sothat process data (e.g., training data, process simulation data, processcontrol data, in-process inspection data, and/or post-build inspectiondata) and process control instructions may be shared and exchangedbetween the different modules. In some embodiments, some of theco-localized system components (e.g., a deposition apparatus and aprocess monitoring tool) may be housed within a local enclosure orhousing (not shown) that enables tighter control of fabricationenvironmental parameters such as temperature, pressure, atmosphericcomposition, etc.

For distributed systems, the sharing of data between one or moredeposition apparatus, one or more process monitoring sensors, machinevision systems, and/or in-process inspection tools may be facilitatedthrough the use of a data compression algorithm, a data featureextraction algorithm, or a data dimensionality reduction algorithm. FIG.14 illustrates one non-limiting example of an unsupervised ANN-basedapproach to image feature extraction and data compression, whereby imagedata is conveniently compressed, transmitted, and reconstructed at adifferent physical/geographical location from that at which it wasacquired.

Processors & Computer Systems:

One or more processors may be employed to implement the machine learningalgorithms, automated object defect classification methods, and additivemanufacturing process control methods disclosed herein. The one or moreprocessors may comprise a hardware processor such as a centralprocessing unit (CPU), a graphic processing unit (GPU), ageneral-purpose processing unit, or computing platform. The one or moreprocessors may be comprised of any of a variety of suitable integratedcircuits, microprocessors, logic devices and the like. Although thedisclosure is described with reference to a processor, other types ofintegrated circuits and logic devices may also be applicable. Theprocessor may have any suitable data operation capability. For example,the processor may perform 512 bit, 256 bit, 128 bit, 64 bit, 32 bit, or16 bit data operations. The one or more processors may be single core ormulti core processors, or a plurality of processors configured forparallel processing.

The one or more processors, or the automated additive manufacturingdeposition apparatus and control system itself, may be part of a largercomputer system and/or may be operatively coupled to a computer network(a “network”) with the aid of a communication interface to facilitatetransmission of and sharing of data and predictive results. The networkmay be a local area network, an intranet and/or extranet, an intranetand/or extranet that is in communication with the Internet, or theInternet. The network in some cases is a telecommunication and/or datanetwork. The network may include one or more computer servers, which insome cases enables distributed computing, such as cloud computing. Thenetwork, in some cases with the aid of the computer system, mayimplement a peer-to-peer network, which may enable devices coupled tothe computer system to behave as a client or a server.

The computer system may also include memory or memory locations (e.g.,random-access memory, read-only memory, flash memory), electronicstorage units (e.g., hard disks), communication interfaces (e.g.,network adapters) for communicating with one or more other systems, andperipheral devices, such as cache, other memory, data storage and/orelectronic display adapters. The memory, storage units, interfaces andperipheral devices may be in communication with the one or moreprocessors, e.g., a CPU, through a communication bus, e.g., as is foundon a motherboard. The storage unit(s) may be data storage unit(s) (ordata repositories) for storing data.

The one or more processors, e.g., a CPU, execute a sequence ofmachine-readable instructions, which are embodied in a program (orsoftware). The instructions are stored in a memory location. Theinstructions are directed to the CPU, which subsequently program orotherwise configure the CPU to implement the methods of the presentdisclosure. Examples of operations performed by the CPU include fetch,decode, execute, and write back. The CPU may be part of a circuit, suchas an integrated circuit. One or more other components of the system maybe included in the circuit. In some cases, the circuit is an applicationspecific integrated circuit (ASIC).

The storage unit stores files, such as drivers, libraries and savedprograms. The storage unit stores user data, e.g., user-specifiedpreferences and user-specified programs. The computer system in somecases may include one or more additional data storage units that areexternal to the computer system, such as located on a remote server thatis in communication with the computer system through an intranet or theInternet.

Some aspects of the methods and systems provided herein, such as thedisclosed object defect classification or additive manufacturing processcontrol algorithms, are implemented by way of machine (e.g., processor)executable code stored in an electronic storage location of the computersystem, such as, for example, in the memory or electronic storage unit.The machine executable or machine readable code is provided in the formof software. During use, the code is executed by the one or moreprocessors. In some cases, the code is retrieved from the storage unitand stored in the memory for ready access by the one or more processors.In some situations, the electronic storage unit is precluded, andmachine-executable instructions are stored in memory. The code may bepre-compiled and configured for use with a machine having one or moreprocessors adapted to execute the code, or may be compiled at run time.The code may be supplied in a programming language that is selected toenable the code to execute in a pre-compiled or as-compiled fashion.

Various aspects of the technology may be thought of as “products” or“articles of manufacture” typically in the form of machine (orprocessor) executable code and/or associated data that is stored in atype of machine readable medium. Machine-executable code may be storedin an optical storage unit comprising an optically readable medium suchas an optical disc, CD-ROM, DVD, or Blu-Ray disc. Machine-executablecode may be stored in an electronic storage unit, such as memory (e.g.,read-only memory, random-access memory, flash memory) or on a hard disk.“Storage” type media include any or all of the tangible memory of thecomputers, processors or the like, or associated modules thereof, suchas various semiconductor memory chips, optical drives, tape drives, diskdrives and the like, which may provide non-transitory storage at anytime for the software that encodes the methods and algorithms disclosedherein.

All or a portion of the software code may at times be communicated viathe Internet or various other telecommunication networks. Suchcommunications, for example, enable loading of the software from onecomputer or processor into another, for example, from a managementserver or host computer into the computer platform of an applicationserver. Thus, other types of media that are used to convey the softwareencoded instructions include optical, electrical and electromagneticwaves, such as those used across physical interfaces between localdevices, through wired and optical landline networks, and over variousatmospheric links. The physical elements that carry such waves, such aswired or wireless links, optical links, or the like, are also consideredmedia that convey the software encoded instructions for performing themethods disclosed herein. As used herein, unless restricted tonon-transitory, tangible “storage” media, terms such as computer ormachine “readable medium” refer to any medium that participates inproviding instructions to a processor for execution.

The computer system typically includes, or may be in communication with,an electronic display for providing, for example, images captured by amachine vision system. The display is typically also capable ofproviding a user interface (UI). Examples of UI's include, but are notlimited to, graphical user interfaces (GUIs), web-based user interfaces,and the like.

Applications:

The disclosed automated object defect classification and adaptive,real-time free form deposition or joining (including additivemanufacturing and welding) process control methods and systems may beused in any of a variety of industrial applications including but notlimited to, the fabrication of parts and assemblies in the automotiveindustry, the aeronautics industry, the medical device industry, theconsumer electronics industry, etc. For example, high volumeapplications for welding processes include use in the automotiveindustry for welding car bodies, as well as use in the oil and gasindustry for construction of wells and refineries, and in the marine(shipbuilding) industry.

EXAMPLES

These examples are provided for illustrative purposes only and notintended to limit the scope of the claims provided herein.

Prophetic Example 1—Automated Object Defect Classification

The machine learning algorithm-based automated object defectclassification methods and systems disclosed herein provide a keycomponent for enabling adaptive, real-time additive manufacturing (orwelding) process control. The methods comprise the use of a machinelearning algorithm to analyze in-process or post-build inspection datafor the purpose of identifying object defects and classifying themaccording to a specified set of fabrication quality criteria, and insome embodiments, further provide input data for real-time adaptiveprocess control.

FIG. 15 provides a schematic illustration of the expected outcome for anunsupervised machine learning process for classification of objectdefects. One or more automated inspection tools, e.g., machine visionsystems coupled with automated image processing algorithms, are used tomonitor and measure feature dimensions, angles, surface finishes, and/orother properties of fabricated parts both in-process and post-build.Defects may be identified, e.g., by removing noise from the inspectiondata and subtracting a reference data set (e.g., a reference image of adefect-free part in the case that machine vision tools are beingutilized for inspection), and classified using an unsupervised machinelearning algorithm such as cluster analysis or an artificial neuralnetwork, to classify individual objects as either meeting or failing tomeet a specified set of decision criteria (e.g., a decision boundary) inthe feature space in which defects are being monitored. Tracking of theprocess control parameters and process monitoring data that were used tofabricate a set of objects (including both those that met the decisioncriteria and those that did not) provides training data for the machinelearning algorithm used to run fabrication process control.

Prophetic Example 2—Adaptive, Real-Time Additive Manufacturing ProcessControl

FIG. 10 shows one non-limiting example of an ANN architecture used forreal-time, adaptive process control of an additive manufacturing (orwelding) process. In FIG. 10, the input layer comprises one or morereal-time streams of process and/or object property data that provide anindication of the current state of the fabrication process and/or thepart being fabricated. Examples of suitable input data streams include,but are not limited to, process simulation data (e.g., FEA simulationdata), process monitoring or characterization data, in-processinspection data, post-build inspection data, or any combination thereof,as well as a list of process control parameters that may be adjusted toimplement next step actions to achieve a target (or future) fabricationstate. This data is fed to the ANN, which in many cases has beenpreviously trained using one or more training data sets comprisingprocess simulation data, process monitoring or characterization data,in-process inspection data, post-build inspection data, or anycombination thereof, from previous fabrication runs of the same ordifferent types of parts. The hidden or intermediate layers of the ANNact as trained feature extractors, while the output layer in the exampleof FIG. 10 provides a determination of a predicted future build state.As noted above, the ANN model is trained to predict future build statebased on current build state and a set of actions. Once the ANN modelhas been developed (i.e., the model can map current state and processparameters to a future state) it's use can be extended to thedetermination of a set of process control parameter adjustments for thenext N states. The ANN model is a first step in creating an action-valuefunction, and determining the next sequence of actions for a given buildstep (as depicted in FIG. 8) is a second step in developing adaptive,real-time process control.

In some embodiments, a neural network model may be used directly todetermine adjustments to process control parameters. This will typicallyinvolve a more difficult “training” or “learning” process. Initially,the machine is allowed to choose randomly from a range of values foreach input process control parameter or action. If the sequence ofprocess control parameter adjustments or actions leads to a flaw ordefect, it is scored as leading to an undesirable (or negative) outcome.Repetition of the process using different sets of randomly chosen valuesfor each process control parameter or action leads to reinforcement ofthose sequences that least to desirable (or positive) outcomes.Ultimately, the neural network model “learns” what adjustments to maketo a set or sequence of deposition process control parameters or actionsin order to achieve the target outcome, i.e., a defect-free printedpart.

Example 3—Post-Process Image Feature Extraction and Correlation withBuild-Time Actions

FIGS. 16A-C provide an example of in-process and post-process imagefeature extraction and correlation of part features with build-timeactions. FIG. 16A: image of the part after the build process has beencompleted. FIG. 16B: example of post-build inspection output (in thiscase, a computerized tomography (CT) scan of the part). FIG. 16C: imageobtained using a feature extraction algorithm to process the CT scanshown in FIG. 16B. In some embodiments, automated feature extractionallows one to correlate part features with build-time actions. Duringthe build (e.g., when printing), in addition to building a machinelearning model that correlates process control parameters (e.g., laserpower, feed rate, travel speed, etc.) and result of the depositionprocess (e.g., the shape of melt pool, defects in the melt pool, etc.),one may also create a mapping between the process control parameters anda specific location in the part. This allows one to subsequently indexpost-build inspection data on the part and correlate findings frompost-build inspection with process control parameters that are specificto a region of interest, thereby expanding the machine learning model toinclude post-build inspection data.

While preferred embodiments of the present invention have been shown anddescribed herein, it will be obvious to those skilled in the art thatsuch embodiments are provided by way of example only. Numerousvariations, changes, and substitutions will now occur to those skilledin the art without departing from the invention. It should be understoodthat various alternatives to the embodiments of the invention describedherein may be employed in any combination in practicing the invention.It is intended that the following claims define the scope of theinvention and that methods and structures within the scope of theseclaims and their equivalents be covered thereby.

1.-20. (canceled)
 21. A method for real-time adaptive control of apost-design free form deposition process or a post-design joiningprocess, the method comprising: a) providing an input design geometryfor an object; b) providing a training data set, wherein the trainingdata set comprises: (i) past process simulation data, past processcharacterization data, past in-process physical inspection data, or pastpost-build physical inspection data, for a plurality of objects thatcomprise at least one object that is different from the object to bephysically fabricated that is provided in step (a); and (ii) trainingdata generated through a repetitive process of randomly choosing valuesfor each of one or more input process control parameters and scoringadjustments to the input process control parameters as leading to eitherundesirable or desirable outcomes, the outcomes based respectively onthe presence or absence of defects detected in a fabricated objectarising from the process control parameter adjustments; c) providing oneor more sensors, wherein the one or more sensors provide real-time datafor one or more object properties as the object is being physicallyfabricated; and d) providing a processor programmed to: (i) predict anoptimal set of one or more process control parameters for initiating thefree form deposition process or joining process, wherein the predictedoptimal set of one or more process control parameters are derived usinga machine learning algorithm that has been trained using the trainingdata set of step (b); (ii) remove noise from the object property dataprovided by the one or more sensors prior to providing it to the machinelearning algorithm; (iii) provide a real-time classification of detectedobject defects using the machine learning algorithm that has beentrained using the training data set of step (b), wherein the real-timedata from the one or more sensors is provided as input to the machinelearning algorithm, and wherein the real-time classification of detectedobject defects is output from the machine learning algorithm; and (iv)provide instructions to perform the post-design free form depositionprocess or post-design joining process to fabricate the object, whereinthe machine learning algorithm adjusts the one or more process controlparameters in real-time while physically performing the free formdeposition process or the joining process.
 22. The method of claim 21,wherein steps (b) through (d) are performed iteratively and processcharacterization data, in-process inspection data, or post-buildinspection data for each iteration is incorporated into the trainingdata set.
 23. The method of claim 21, wherein the free form depositionprocess or joining process is a stereolithography (SLA), digital lightprocessing (DLP), fused deposition modeling (FDM), selective lasersintering (SLS), selective laser melting (SLM), electronic beam melting(EBM), or welding process.
 24. The method of claim 21, wherein themachine learning algorithm comprises an artificial neural networkalgorithm, a Gaussian process regression algorithm, a logistical modeltree algorithm, a random forest algorithm, a fuzzy classifier algorithm,a decision tree algorithm, a hierarchical clustering algorithm, ak-means algorithm, a fuzzy clustering algorithm, a deep Boltzmannmachine learning algorithm, a deep convolutional neural networkalgorithm, a deep recurrent neural network, or any combination thereof.25. The method of claim 21, wherein the method is implemented usingeither: (i) a single integrated system comprising a deposition orjoining apparatus, a sensor, and a processor; or (ii) a distributed,modular system comprising a first deposition or joining apparatus, afirst sensor, and a first processor, wherein the first deposition orjoining apparatus, the first sensor, and the first processor areconfigured to share training data and real-time process characterizationdata via a local area network (LAN), an intranet, an extranet, or aninternet.
 26. The method of claim 21, wherein the training data setfurther comprises process characterization data, in-process inspectiondata, or post-build inspection data that is generated by an operatorwhile manually adjusting the input process control parameters.
 27. Themethod of claim 21, wherein the one or more sensors comprise at leastone laser interferometer, machine vision system, or sensor that detectselectromagnetic radiation that is reflected, scattered, absorbed,transmitted, or emitted by the object.
 28. The method of claim 21,wherein the one or more sensors provide data on acoustic energy ormechanical energy that is reflected, scattered, absorbed, transmitted,or emitted by the object.
 29. The method of claim 21, wherein the objectdefects are detected as differences between object property data and areference data set that are larger than a specified threshold, and areclassified using a one-class support vector machine (SVM) or autoencoderalgorithm.
 30. The method of claim 21, wherein the object defects aredetected and classified using an unsupervised one-class support vectormachine (SVM), autoencoder, clustering, or nearest neighbor (kNN)machine learning algorithm and a training data set that comprises objectproperty data for defective and defect-free objects.
 31. A system forcontrolling a post-design free form deposition process or a post-designjoining process, the system comprising: a) a first deposition or joiningapparatus for physically fabricating an object based on an input designgeometry; b) one or more process characterization sensors, wherein theone or more process characterization sensors provide real-time data forone or more process parameters or object properties; and c) a processorprogrammed to: (i) provide a predicted optimal set of one or moreprocess control parameters for initiating the free form depositionprocess or joining process using a machine learning algorithm; (ii)remove noise from the real-time object property data provided by the oneor more process characterization sensors prior to providing it to themachine learning algorithm; (iii) provide a real-time classification ofobject defects using the machine learning algorithm, wherein thereal-time object property data from the one or more processcharacterization sensors is provided as input to the machine learningalgorithm, and wherein the real-time classification of detected objectdefects is output from the machine learning algorithm; and (iv) provideinstructions to perform the post-design free form deposition process orpost-design joining process to fabricate the object, wherein the machinelearning algorithm adjusts the one or more process control parameters inreal-time while physically performing the free form deposition processor the joining process, and wherein the machine learning algorithm hasbeen trained using a training data set that comprises: i) past processsimulation data, past process characterization data, past in-processphysical inspection data, or past post-build physical inspection data,for a plurality of objects that comprise at least one object that isdifferent from the object to be physically fabricated that is providedin step (a); and ii) training data generated through a repetitiveprocess of randomly choosing values for each of one or more inputprocess control parameters and scoring adjustments to the input processcontrol parameters as leading to either undesirable or desirableoutcomes, the outcomes based respectively on the presence or absence ofdefects detected in a fabricated object arising from the process controlparameter adjustments.
 32. The system of claim 31, wherein the firstdeposition or joining apparatus, the one or more processcharacterization sensors, and the processor are configured as: (i) asingle integrated system; or (ii) as distributed system modules thatshare training data and real-time process characterization data via alocal area network (LAN), an intranet, an extranet, or an internet. 33.The system of claim 31, wherein the one or more process characterizationsensors comprise at least one laser interferometer, machine visionsystem, or sensor that detects electromagnetic radiation that isreflected, scattered, absorbed, transmitted, or emitted by the object.34. The system of claim 31, wherein the one or more processcharacterization sensors provide data on acoustic energy or mechanicalenergy that is reflected, scattered, absorbed, transmitted, or emittedby the object.
 35. The system of claim 31, wherein the object defectsare detected as differences between object property data and a referencedata set that are larger than a specified threshold, and are classifiedusing a one-class support vector machine (SVM) or autoencoder algorithm.36. The system of claim 31, wherein the object defects are detected andclassified using an unsupervised one-class support vector machine (SVM),autoencoder, clustering, or nearest neighbor (kNN) machine learningalgorithm and a training data set that comprises object property datafor defective and defect-free objects.
 37. The system of claim 31,wherein the first deposition or joining apparatus is a stereolithography(SLA) apparatus, digital light processing (DLP) apparatus, fuseddeposition modeling (FDM) apparatus, selective laser sintering (SLS)apparatus, selective laser melting (SLM) apparatus, electronic beammelting (EBM) apparatus, or welding apparatus.
 38. The system of claim31, wherein the machine learning algorithm comprises an artificialneural network algorithm, a Gaussian process regression algorithm, alogistical model tree algorithm, a random forest algorithm, a fuzzyclassifier algorithm, a decision tree algorithm, a hierarchicalclustering algorithm, a k-means algorithm, a fuzzy clustering algorithm,a deep Boltzmann machine learning algorithm, a deep convolutional neuralnetwork algorithm, a deep recurrent neural network, or any combinationthereof.
 39. The system of claim 31, wherein the training data setfurther comprises process characterization data, in-process inspectiondata, or post-build inspection data that is generated by an operatorwhile manually adjusting the process control parameters.